proteoQ

Introduction to proteoQ

Chemical labeling using tandem mass tag (TMT) and label-free quantitaion (LFQ) have been commonly applied in mass spectrometry (MS)-based quantification of proteins and peptides. The proteoQ tool is designed for automated and reproducible analysis of proteomics data. It interacts with an Excel spread sheet for dynamic sample selections, aesthetics controls and statistical modelings. It further integrates the operations against data rows and columns into functions at the users’ interface. The arrangements allow users to put ad hoc manipulation of data behind the scene and instead apply metadata to openly address biological questions using various data preprocessing and informatic tools. In addition, the entire workflow is documented and can be conveniently reproduced upon revisiting.

The framework of proteoQ consists of data processing and informatics analysis. It first processes the peptide spectrum matches (PSM) tables from Mascot, MaxQuant and Spectrum Mill searches, for 6-, 10- 11- or 16-plex TMT experiments using Thermo’s Orbitrap mass analyzers. It is also capable of processing the LFQ data from MaxQuant. Peptide and protein results are then produced with users’ selection of parameters in data filtration, alignment and normalization. The package further offers a suite of tools and functionalities in statistics, informatics and data visualization by creating ‘wrappers’ around published R routines.¹

(Click Recent Posts for additional examples.)

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Installation

To install this package, start R (version “4.0”) and enter:²

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("qzhang503/proteoQ")

1 Data normalization

In this document, I (Qiang Zhang) first illustrate the following applications of proteoQ:

• Summarization of PSM results to normalized peptide and protein data.
• Visualization of quality metrics in normalized peptide and protein data.
• Re-normalization of data against selected samples.
• Mixed-bed normalization using full or partial data.
• Removal of low-quality entries from PSM, peptide and protein data.

The data set we will use in this section corresponds to the proteomics data from Mertins et al. (2018). In the study, two different breast cancer subtypes, triple negative (WHIM2) and luminal (WHIM16), from patient-derived xenograft (PDX) models were assessed by three independent laboratories. At each site, lysates from WHIM2 and WHIM16 were each split and labeled with 10-plex TMT at equal sample sizes and repeated on a different day. This results in a total of 60 samples labeled under six 10-plex TMT experiments. The samples under each 10-plex TMT were fractionated by off-line, high pH reversed-phase (Hp-RP) chromatography, followed by LC/MS analysis. The MS data were analyzed against the search engines of Mascot, MaxQuant and Spectrum Mill. Ten percent of the PSM entries were sampled randomly from the complete data sets and stored in a companion package, proteoQDA.

1.1 Experiment setup

The data packages, proteoQDA, should have been made available through the proteoQ installation.³

1.1.1 Fasta databases

RefSeq databases of human and mouse were used in the MS/MS searches against the WHIM data sets. To properly annotate protein entries with proteoQ, we would need the fasta file(s) that were used in the database searches.⁴ In the example below, we copy over the corresponding fasta files from the proteoQDA to a database folder:

library(proteoQDA)
copy_refseq_hs("~/proteoQ/dbs/fasta/refseq")
copy_refseq_mm("~/proteoQ/dbs/fasta/refseq")

1.1.2 PSM data

The data processing begins with PSM table(s) from Mascot, MaxQuant or Spectrum Mill with the following compilation in file names:

• Mascot: begin with letter F, followed by digits and ends with .csv;
• MaxQuant: start with msms and end with .txt;
• Spectrum Mill: start with PSMexport and end with .ssv.

The corresponding PSMs are available through one of the followings copy_ utilities:

# Mascot
copy_global_mascot()

# or MaxQuant
copy_global_maxquant()

# or Spectrum Mill
copy_global_sm()

To illustrate, I copy over Mascot PSMs to a working directory, dat_dir:⁵

dat_dir <- "~/proteoQ/examples"
dir.create(dat_dir, recursive = TRUE, showWarnings = FALSE)
copy_global_mascot(dat_dir)

1.1.3 Metadata

The workflow involves an Excel template containing the metadata of multiplex experiments, including experiment numbers, TMT channels, LC/MS injection indices, sample IDs, reference channels, RAW MS data file names and additional fields from users. The default file name for the experimental summary is expt_smry.xlsx. If samples were fractionated off-line prior to LC/MS, a second Excel template will also be filled out to link multiple RAW MS file names that are associated to the same sample IDs. The default file name for the fractionation summary is frac_smry.xlsx.⁶ Unless otherwise mentioned, we will assume these default file names throughout the document.

Columns in the expt_smry.xlsx are approximately divided into the following three tiers: (1) essential, (2) optional default and (3) optional open. We supply the required information of the TMT experiments under the essential columns. The optional default columns serve as the fields for convenient lookups in sample selection, grouping, ordering, aesthetics etc. For instance, the program will by default look for values under the Color column if no instruction was given in the color coding of a PCA plot. The optional open fields on the other hand allow us to define our own analysis and aesthetics. For instance, we may openly define multiple columns of contrasts at different levels of granularity for uses in statistical modelings. Description of the column keys can be found from the help document by entering ?proteoQ::load_expts from a R console.

We next copy over a pre-compiled expt_smry.xlsx and a frac_smry.xlsx to the working directory:

copy_global_exptsmry(dat_dir)
copy_global_fracsmry(dat_dir)

We now have all the pieces that are required by proteoQ in place. Let’s have a quick glance at the expt_smry.xlsx file. We note that no reference channels were indicated under the column Reference. With proteoQ, the log2FC of each species in a given sample is calculated either (a) in relative to the reference(s) within each multiplex TMT experiment or (b) to the mean of all samples in the same experiment if reference(s) are absent. Hence, the later approach will be employed to the exemplary data set that we are working with. In this special case, the mean(log2FC) for a given species in each TMT experiment is averaged from five WHIM2 and five WHIM16 aliquots, which are biologically equivalent across TMT experiments.

1.1.4 Experiment upload

As a final step of the setup, we will load the experimental summary into a work space:

library(proteoQ)
load_expts("~/proteoQ/examples")

1.2 PSM summarization

PSMs are MS/MS events that lead to peptide identification at certain confidence levels. The evidences in PSMs can then be summarized to peptide and protein findings using various descriptive statistics. In this section, we will apply proteoQ to summarize PSM data into peptide and protein reports.

1.2.1 normPSM

We start the section by processing the PSM files exported directly from Mascot searches:

# columns keys in PSM files suitable for varargs of `filter_`
normPSM(
  group_psm_by = pep_seq_mod, 
  group_pep_by = gene, 
  fasta = c("~/proteoQ/dbs/fasta/refseq/refseq_hs_2013_07.fasta", 
            "~/proteoQ/dbs/fasta/refseq/refseq_mm_2013_07.fasta"), 
  rptr_intco = 1000,
  rm_craps = TRUE,
  rm_krts = FALSE,
  rm_outliers = FALSE, 
  annot_kinases = TRUE, 
  plot_rptr_int = TRUE, 
  plot_log2FC_cv = TRUE, 
  
  filter_psms = exprs(pep_expect <= .1, pep_score >= 15), 
  filter_more_psms = exprs(pep_rank == 1),
)

At group_psm_by = pep_seq, PSM entries with the same primary peptide sequence but different variable modifications will be grouped for analysis using descriptive statistics. In case group_psm_by = pep_seq_mod, PSMs will be grouped alternatively according to the unique combination of the primary sequences and the variable modifications of peptides. Analogously, group_pep_by specify the grouping of peptides by either protein accession names or gene names. The fasta argument points to the location of a copy of the RefSeq fasta files that were used in the corresponding MS/MS searches. Additional options include rm_craps, rm_krts, annot_kinases etc. More description of normPSM can be found by accessing its help document via ?normPSM.

Every time the normPSM module is executed, it will process the PSM data from the ground up. In other words, it has no memory on prior happenings. For instance, after inspecting graphically the intensity distributions of reporter ions at plot_rptr_int = TRUE, we may consider a more inclusive cut-off at rptr_intco = 100. The downward in rptr_intco is not going to cause information loss in the range of 100 to 1,000. This is trivia but worth mentioning here. As we will find out in following sections, utilities in peptide and protein normalization, standPep and standPrn, do pass information onto successive iterations.

1.2.2 Outlier samples

For experiments that are proximate in the quantities of input materials, there might still be unprecedented events that could have caused dipping in the ranges of reporter-ion intensity for certain samples. With proper justification, we might consider excluding the outlier samples from further analysis. The sample removal and PSM re-processing can be achieved by simply deleting the corresponding entries under the column Sample_ID in expt_smry.xlsx, followed by the re-execution of normPSM() (See additional notes on data exclusion and metadata for LFQ).

1.2.3 Outlier data entries

There is a subtle problem when we choose to remove PSM outliers at rm_outliers = TRUE. Note that PSM outliers will be assessed at a per-peptide-and-per-sample basis, which can be a slow process for large data sets. To circumvent repeated efforts in finding PSM outliers, we may initially set rm_outliers = FALSE and plot_rptr_int = TRUE when executing normPSM(). This will allow us to first decide on an ultimate threshold of reporter-ion intensity, before proceeding to the more time-consuming procedure in PSM outlier removals.

1.2.4 Variable arguments

The normPSM function can take additional, user-defined arguments of dot-dot-dot (see Wickham 2019, ch. 6) for the row filtration of data using logical conditions. In the above example, we have limited ourselves to PSM entries with pep_expect <= 0.1 and pep_score >= 15 by supplying the variable argument (vararg) of filter_psms_at. We further filtered the data at pep_rank == 1 with another vararg of filter_psms_more. It makes no difference whether we put the conditions in one or multiple statements:

normPSM(
  filter_psms_at = exprs(pep_expect <= .1, pep_score >= 15, pep_rank == 1), 
  ..., 
)

The creation and assignment of varargs need to follow a format of filter_blahblah = exprs(cdn1, cdn2, ..., cdn_last). Note that the names of varargs on the lhs start with the character string of filter_ to indicate the task of data filtration. On the rhs, pep_expect, pep_score and pep_rank are column keys that can be found from the Mascot PSM data. Backticks will be needed for column keys containing white space(s) and/or special character(s): `key with space (sample id in parenthesis)`. Analogously, we can apply the vararg approach to MaxQuant and Spectrum Mill PSMs:

# `PEP` and `Mass analyzer` are column keys in MaxQuant PSM tables
normPSM(
  filter_psms_at = exprs(PEP <= 0.1, `Mass analyzer` == "FTMS"), 
  ..., 
)

# `score` is a column key in Spectrum Mill PSM tables
normPSM(
  filter_psms_at = exprs(score >= 10), 
  ..., 
)

I am new to R. It looks like that canonical R does not support the straight assignment of logical expressions to function arguments. To get around this, I took advantage of the facility of non-standard evaluation in rlang package in that the logical conditions are supplied within the round parenthesis after exprs. Next, the proteoQ program will obtain the expression(s) on the rhs of each vararg statement by performing a bare evaluation using rlang::eval_bare. Following that, a tidy evaluation by rlang::eval_tidy will be coupled to a local facility in proteoQ to do the real work of data filtrations ((see Wickham 2019, ch. 20)).

The approach of data filtration taken by normPSM might at first looks strange; however, it allows me to perform data filtration in a integrated way. As mentioned in the beginning, a central theme of proteoQ is to reduce or avoid direct data manipulations but utilizes metadata to control both data columns and rows. With the self-containedness in data filtration (and data ordering later), I can readily recall and reproduce what I had done when revisiting the system after an extended peroid. Otherwise, I would likely need ad hoc operations by mouse clicks or writing ephemeral R scripts, and soon forget what I have done.

Moreover, the build-in approach can serve as building blocks for more complex data processing. As shown in the help documents via ?standPep and ?standPrn, we can readily perform mixed-bed normalization by sample groups, against either full or partial data.

1.2.5 Vararg choices

With normPSM, we can pretty much filter_ data under any PSM columns we like. In the above Mascot example, I have chosen to filter PSM entires by their pep_expect, pep_score etc. There is a reason for this.

Let’s first consider a different column pep_len. The values underneath are unique to both PSMs and peptides. As you might courteously agree, its time has not yet come in terms of tentative data filtration by peptide length. In other words, we can delay the filtration of peptide entries by their sequence lengths when we are actually working with peptide data. The summarization of PSMs to peptides is not going to change the number of amino acid residues in peptides. By contrast, the data under pep_expect are unique to PSMs, but not necessary to peptides. This is obvious in that each of the PSM events of the same peptide is likely to have its own confidence expectation in peptide identification. Therefore, if we were to filter data by their pep_expect values at a later stage of analysis, we would have lost the authentic information in pep_expect for peptides with multiple PSM identifications. More specifically, the values under pep_expect in peptide tables are the geometric-mean representation of PSM results (see also section 4).

For this reason, I named the varargs filter_psms_at and filter_psms_more in the above normPSM examples. This allows me to readily recall that I was filtering data based on criteria that are specific to PSMs.

1.2.6 Varargs and data files

Vararg statements of filter_ and arrange_ are available in proteoQ for flexible filtration and ordering of data rows. To take advantage of the feature, we need to be aware of the column keys in input files. As indicated by their names, filter_ and filter2_ perform row filtration against column keys from a primary data file, df, and secondary data file(s), df2, respectively (df and df2 defined here). The same correspondence is applicable for arrange_ and arrange2_ varargs.

Users will typically employ either primary or secondary vararg statements, but not both. In the more extreme case of gspaMap(...), it links prnGSPA(...) findings in df2 to the significance p-values and abundance fold changes in df for volcano plot visualization by gene sets.

1.2.7 purgePSM

To finish our discussion of PSM processing, let us consider having one more bash in data cleanup. The corresponding utility is purgePSM. It performs data purging by the CV of peptides, measured from contributing PSMs within the same sample. Namely, quantitations that have yielded peptide CV greater than a user-supplied cut-off will be replaced with NA.

The purgePSM utility reads files \PSM\TMTset1_LCMSinj1_PSM_N.txt, TMTset1_LCMSinj2_PSM_N.txt etc. from a preceding step of normPSM. To revert programmatically the changes made by purgePSM, we would need to start over with normPSM. Alternatively, we may make a temporary copy of these files for a probable undo.

This process takes place sample (column)-wisely while holding the places for data points that have been nullified. It is different to the above row filtration processes by filter_ in that there is no row removals with purging, not until all-NA rows are encountered.

Earlier in section 1.2.1, we have set plot_log2FC_cv = TRUE by default when calling normPSM. This will plot the distributions of the CV of peptide log2FC. In the event of plot_log2FC_cv = FALSE, we can have a second chance in visualizing the distributions of peptide CV before any permanent data nullification:

purgePSM ()

Taking the sample entries under TMT_Set one and LCMS_Injection one in label_scheme.xlsx as an example, we can see that a small portion of peptides have CV greater than 0.5 at log2 scale (Figure 1A).

Figure 1A-1C. CV of peptide log2FC (based on full data set). Left: no CV cut-off; middle: CV cut-off at 0.5; right: CV cut-off at 95 percentile.

Quantitative differences greater than 0.5 at a log2 scale is relatively large in TMT experiments,⁷ which can be in part ascribed to a phenomenon called peptide co-isolation and co-fragmentation in reporter ion-based MS experiments. We might, for instance, perform an additional cleanup by removing column-wisely data points with CV greater than 0.5 (Figure 1B):

purgePSM (
  max_cv = 0.5,
)

The above method using a flat cut-off would probably fall short if the ranges of CV are considerably different across samples (see Lab 3.1). Alternatively, we can remove low-quality data points using a CV percentile, let’s say at 95%, for each sample (Figure 1C):

# copy back `\PSM\TMTset1_LCMSinj1_PSM_N.txt` etc. before proceed
# otherwise the net effect will be additive to the prior(s)
purgePSM (
  pt_cv = 0.95,
)

In the event of both pt_cv and max_cv being applied to nullify data, they follow the precedence of pt_cv > max_cv. When needed, we can overrule the default by executing purgePSM sequentially at a custom order:

# at first no worse than 0.5
purgePSM (
  max_cv = 0.5,
)

# next `pt_cv` on top of `max_cv`
purgePSM (
  pt_cv = 0.95,
)

The data purge is also additive w.r.t. to repetative analysis. In the following example, we are actually perform data cleanup at a CV threshold of 90%:

# at first 95%
purgePSM (
  pt_cv = 0.95,
)

# next 95% of 95%
purgePSM (
  pt_cv = 0.95,
)

While multiple PSMs carry information about the precision in peptide measures, the above single-sample variance does not inform sampling errors prior to peptide separations. For instance, the same peptide species from a given sample remain indistinguishable/exchangeable prior to the off-line fractionation. As a result, the CV shown by normPSM or purgePSM mainly tell us the uncertainty of measures beyond the point of peptide parting.

NB: CV is sensitive to outliers and some large CV in peptide quantitations may be merely due to a small number of bad measures. Although the option of rm_outliers was set to FALSE during our earlier call to normPSM, I think it is generally a good idea to have rm_outliers = TRUE.

1.3 PSMs to peptides

In this section, we summarise the PSM results to peptides with PSM2Pep, mergePep, standPep and optional purgePep.

1.3.1 PSM2Pep

The utility for the summary of PSMs to peptides is PSM2Pep:

PSM2Pep()

It loads the PSM tables from the preceding normPSM procedure and summarize them to peptide data using various descriptive statistics (see also Section 4). For intensity and log2FC data, the summarization method is specified by argument method_psm_pep, with median being the default.

1.3.2 mergePep

Following the summarization of PSMs to peptides, the utility mergePep will assemble individual peptide tables, Peptide\TMTset1_LCMSinj1_Peptide_N.txt, TMTset1_LCMSinj2_Peptide_N.txt etc., into one larger piece, Peptide.txt.

mergePep(
  filter_peps_by = exprs(pep_len <= 100),
)

Similar to normPSM, we can filter data via column keys linked to the varargs of filter_. In the exemplary vararg statement of filter_peps_by, we exlcude longer peptide sequences with more than 100 amino acid residues. If we are interested in human, but not mouse, peptides from the pdx samples, we can specify similarly that species == "human". Sometimes, it may remain unclear on proper data filtration at the early stage of analysis. In that case, we may need additional quality assessments that we will soon explore. Alternatively, we may keep as much information as possible and apply varargs in downstream analysis.

Note that pep_len is a column key in TMTset1_LCMSinj1_Peptide_N.txt with Mascot workflows. Depends on the search engines, we might need to employ different key names for the same purpose:

# `Length` in a column key in TMTset1_LCMSinj1_Peptide_N.txt with MaxQuant
mergePep(filter_peps_at = exprs(Length <= 100))

1.3.3 standPep

The utility standPep standardizes peptide results from mergePep with additional choices in data alignment.

standPep(
  range_log2r = c(10, 90), 
  range_int = c(5, 95),   
  method_align = MGKernel, 
  n_comp = 3, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
)

The parameters range_log2r and range_int outline the ranges of peptide log2FC and reporter-ion intensity, respectively, for use in defining the CV and scaling the log2FC across samples. The log2FC of peptide data will be aligned by median centering across samples by default. If method_align = MGKernel is chosen, log2FC will be aligned under the assumption of multiple Gaussian kernels.⁸ The companion parameter n_comp defines the number of Gaussian kernels and seed set a seed for reproducible fittings. Additional parameters, such as, maxit and epsilon, are defined in and for use with normalmixEM.

It is also feasible to perform standPep against defined sample columns and data rows. Moreover, the utility can be applied interactively with cumulative effects. Combinations and iterations of the features can lead to specialty sample alignments that will discuss soon (sections 1.3.5 - 1.3.7). Before delving more into the details, we would probably need some helps from the pepHist utility in the immediately following.

1.3.4 pepHist

The pepHist utility plots the histograms of peptide log2FC. It further bins the data by their contributing reporter-ion intensity. In the examples shown below, we compare the log2FC profiles of peptides with and without scaling normalization:⁹

# without scaling
pepHist(
  scale_log2r = FALSE, 
  ncol = 10,
)

# with scaling  
pepHist(
  scale_log2r = TRUE, 
  ncol = 10,
)

1.3.4.1 Sample subset (col_select)

By default, the above calls of pepHist will look for none void entries under column Select in expt_smry.xlsx. This will results in histogram plots with 60 panels in each, which may not be easy to explore as a whole. In stead, we will break the plots down by their data origins. We begin with modifying the expt_smry.xlsx file by adding the columns BI_1, JHU_1 etc. Each of the new columns includes sample entries that are tied to their laboratory origins and TMT batches (the columns are actually already in the expt_smry.xlsx).

We now are ready to plot histograms for each subset of the data. In this document, we only display the plots using the BI_1 subset:

# without scaling 
pepHist(
  scale_log2r = FALSE, 
  col_select = BI_1,
  ncol = 5,
  filename = bi1_n.png, 
)

# with scaling 
pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filename = bi1_z.png, 
)

NB: We interactively told pepHist() that we are interested in sample entries under the newly created BI_1 column. Behind the scene, the interactions are facilitated by openxlsx via the reading of the Setup workbook in expt_smry.xlsx. We also supply a file name, assuming that we want to keep the previously generated plots with default file names of Peptide_Histogram_N.png and Peptide_Histogram_Z.png.

Figure 2A-2B. Histograms of peptide log2FC. Top: scale_log2r = FALSE; bottom, scale_log2r = TRUE

As expected, both the widths and the heights of log2FC profiles become more comparable after the scaling normalization. However, such adjustment may cause artifacts when the standard deviation across samples are genuinely different. I typically test scale_log2r at both TRUE and FALSE, then make a choice in data scaling together with my a priori knowledge of the characteristics of both samples and references.¹⁰ We will use the same data set to illustrate the impacts of reference selections in scaling normalization in Lab 3.1.

1.3.4.2 Side effects

It should also be noted that the curves of Gaussian density in histograms are calculated during the latest call to standPep(...) with the option of method_align = MGKernel. There is a useful side effect when comparing leading and lagging profiles of log2FC. In the following bare-bones example, we align differently the peptide log2FC with the default method of median centering:

standPep()

We then visualize the histograms of the ratio profiles (Figure 2C):

pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filename = bi1_z_mc.png, 
)

Within this document, the preceding example that involves standPep(...) at method_align = MGKernel is given in section 1.3.3. In this case, a comparison between the present and the prior histograms will reveal the difference in ratio alignments between a median centering and a three-Gaussian assumption. More examples in the side effects can be found from the help document via ?standPep and ?pepHist.

Figure 2C-2D. Histograms of peptide log2FC. Top: median-centering for all samples; bottom: W2.BI.TR2.TMT1 aligned differently by Gaussian density

1.3.4.3 Visualization of data subsets (filter_)

The varargs of filter_ are also available in the pepHist utility. With the following examples, we can visualize the peptide log2FC with human and mouse origins, respectively:

pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filter_by_sphu = exprs(species == "human"),
  filename = hs.png, 
)

pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filter_by_spmm = exprs(species == "mouse"),
  filename = mm.png, 
)

1.3.5 standPep(col_select = …)

Now that we have been acquainted with pepHist, let’s revisit and explore additionally standPep with its features in normalizing data against defined sample columns (and data rows in the following sections).

Needs in data (re)normalization may be encountered more often than not. One of the trivial circumstances is that a multi-Gaussian kernel can fail capturing the log2FC profiles for a subset of samples. This is less an issue with a small number of samples. Using a trial-and-error approach, we can start over with a new combination of parameters, such as a different seed, and/or a different range of range_log2r etc. However, the one-size-fit-all attempt may remain inadequate when the number of samples is relatively large. The proteoQ allows users to focus fit against selected samples. This is again the job of argument col_select. Let’s say we want to re-fit the log2FC for samples W2.BI.TR2.TMT1 and W2.BI.TR2.TMT2. We simply add a column, which I named it Select_sub, to expt_smry.xlsx with the sample entries for re-fit being indicated under the column:

We may then execute the following codes with argument col_select being linked to the newly created column:

standPep(
  method_align = MGKernel, 
  range_log2r = c(10, 90), 
  range_int = c(5, 95), 
  n_comp = 3, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
  col_select = Select_sub,
)

pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filename = mixed_bed_3.png, 
)

In the preceding execution of bare-bones standPep(), samples were aligned by median centering (Figure 2C). As expected, the current partial re-normalization only affects samples W2.BI.TR2.TMT1 and W2.BI.TR2.TMT2 (Figure 2D, W2.BI.TR2.TMT2 not shown). In other words, samples W2.BI.TR2.TMT1 and W2.BI.TR2.TMT2 are now aligned by their Gaussian densities whereas the remaining are by median centering. The combination allows us to align sample by mixed-bedding the MC or the MGKernel method.

1.3.6 standPep(slice_ = …)

We have previously applied the varargs of filter_ in normPSM and mergePep to subset data rows. With this type of arguments, data entries that have failed the filtration criteria will be removed for indicated analysis.

Similarly, we employed the filter_ varargs in pepHist to subset peptides with human or mouse origins (section 1.3.4.3). This is often not an issue in informatic analysis and visualization, as we do not typically overwrite the altered inputs on external devices at the end. Sometimes we may however need to carry out similar tasks based on partial inputs and update the complete set of data for future uses. One of the circumstances is model parameterization by a data subset and to apply the finding(s) to update the complete set.

The standPep utility accepts variable arguments of slice_. The vararg statement(s) identify a subset of data rows from the Peptide.txt. The partial data will be taken for parameterizing the alignment of log2FC across samples. In the hypothetical example shown below, we normalize peptide data based peptide entries with sequence lengths greater than 10 and smaller than 30. The full data set will be updated accordingly with the newly derived parameters. Different to the filter_ varargs, there is no data entry removals from the complete data set with the slice_ procedure.

## DO NOT RUN
standPep(
  ...,
  slice_peps_by = exprs(pep_len > 10, pep_len < 30),
)
## END of DO NOT RUN

The varargs are termed slice_ to make distinction to filter_. Although it might at first seem a little involved, the underlying mechanism is simple: col_select defines the sample columns and slice_ defines the data rows in Peptide.txt; and only the intersecting area between columns and rows will be subject additively to the parameterization in data alignment. The same pattern will be applied every time we invoke standPep.

Just like col_select and filter_ in pepHist, the combination in fixed argument col_select and variable argument slice_ can lead to features in versatile data processing. Several working examples are detailed and can be accessed via ?standPep and ?standPrn.¹¹

1.3.7 Housekeepers

Now it becomes elementary if we were to normalize data against housekeeping protein(s). Let’s say we have GAPDH in mind as a housekeeping invariant among the proteomes, and of course we have good accuracy in their log2FC. We simply slice the peptide entries under GAPDH out for use as a normalizer:

standPep(
  method_align = MC, 
  range_log2r = c(10, 90), 
  range_int = c(5, 95), 
  col_select = Select_sub,
  slice_hskp = exprs(gene %in% c("GAPDH")),
)

pepHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filename = housekeepers.png, 
)

Note that I chose method_align = MC in the above. There are only a few rows available for the samples linked to col_select, after slicing out GAPDH! The number of data points is too scare for fitting the selected samples against a 3-component Gaussian. A more detailed working example can also be found via ?standPep where you would probably agree that GAPDH is actually not a good normalizer for the data set.¹²

1.3.8 purgePep

Analogously to the PSM processing, we may nullify data points of peptides by specifying a cut-off in their protein CVs:

# no purging
purgePep()

# or purge column-wisely by max CV
purgePep (
  max_cv = 0.5,
  filename = "by_maxcv.png",  
)

# or purge column-wisely by CV percentile
# remember the additive effects
purgePep (
  pt_cv = 0.5,
  filename = "by_ptcv.png",
)

NB: The above single-sample CVs of proteins are based on ascribing peptides, which thus do not inform the uncertainty in sample handling prior to the parting of protein entities, for example, the enzymatic breakdown of proteins in a typical MS-based proteomic workflow. On the other hand, the peptide log2FC have been previously summarized by the median statistics from contributing PSMs. Putting these two together, the CV by purgePep describes approximately the uncertainty in sample handling from the breakdown of proteins to the off-line fractionation of peptides.

1.4 Peptides to proteins

In this section, we summarize peptides to proteins, for example, using a two-component Gaussian kernel and customized filters.

1.4.1 Pep2Prn

The utility for the summary of peptides to proteins is Pep2Prn:

Pep2Prn()

It loads the Peptide.txt and summarize the peptide data to interim protein results in Protein.txt, using various descriptive statistics (see also Section 4). For intensity and log2FC data, the summarization method is specified by argument method_pep_prn, with median being the default.

The utitily also accept varargs of filter_ for data row filtration against the column keys in Peptide.txt.

1.4.2 standPrn

The utility standPrn standardizes protein results from Pep2Prn with additional choices in data alignment.

standPrn(
  range_log2r = c(10, 90), 
  range_int = c(5, 95),   
  method_align = MGKernel, 
  n_comp = 2, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
  slice_prots_by = exprs(prot_n_pep >= 2),
)

It loads Protein.txt from Pep2Prn or a preceding standPrn procedure and align protein data at users’ choices. The utility is analogous to standPep with choices in col_select and slice_. In the above example, the normalization is against proteins with two more identifying peptides. For helps, try ?standPrn.

1.4.3 prnHist

Similar to the peptide summary, we can inspect the alignment and the scale of ratio profiles for protein data:

# without scaling
prnHist(
  scale_log2r = FALSE, 
  col_select = BI_1,
  ncol = 5,
  filename = bi1_n.png, 
)

# with scaling
prnHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  filename = bi1_z.png, 
)

For simplicity, we only display the histograms with scaling normalization (Figure 2E).

Figure 2E-2F. Histograms of protein log2FC at scale_log2r = TRUE. Left: before filtration; right, after filtration

1.4.3.2 Side effects

In section 1.3.4.2, we used pepHist to illustrate the side effects in histogram visualization when toggling the alignment methods between MC and MGKernel. In the following, we will show another example of side effects using the protein data.

We prepare the ratio histograms for proteins with ten or more quantifying peptides:

# without scaling
prnHist(
  scale_log2r = FALSE, 
  col_select = BI_1,
  ncol = 5,
  
  filter_prots_by = exprs(prot_n_pep >= 10),
  filename = bi1_n_npep10.png, 
)

# with scaling
prnHist(
  scale_log2r = TRUE, 
  col_select = BI_1,
  ncol = 5,
  
  filter_prots_by = exprs(prot_n_pep >= 10),
  filename = bi1_z_npep10.png, 
)

The density curves are based on the latest call to standPrn(...) with method_align = MGKernel (Figure 2E). For simplicity, we again only show the current plots at scale_log2_r = TRUE (Figure 2F). The comparison between the lead and the lag allows us to visualize the heteroscedasticity in data and in turn inform new parameters in data renormalization.

1.4.4 scale_log2_r

Up to this point, we might have reach a consensus on the choice of scaling normalization. If so, it may be plausible to set the value of scale_log2r under the Global environment, which is typically the R console that we are interacting with.

# if agree
scale_log2r <- TRUE

# or disagree
scale_logr <- FALSE

In this way, we can skip the repetitive setting of scale_log2r in our workflow from this point on, and more importantly, prevent ourselves from peppering the values of TRUE or FALSE in scale_log2r from analysis to analysis.

1.5 Workflow scripts

Scripts that were used in this document can be accessed via:

system.file("extdata", "workflow_base.R", package = "proteoQ")

Another good place to get started is via the help ?load_expts. More workflow scripts are under construction.

1.6 Quick start

For quick demonstrations, steps in data preprocessing can be bypassed:

unzip(system.file("extdata", "demo.zip", package = "proteoQDA"), 
      exdir = "~/proteoq_bypass", overwrite  = FALSE)

# file.exists("~/proteoq_bypass/proteoQ/examples/Peptide/Peptide.txt")
# file.exists("~/proteoq_bypass/proteoQ/examples/Protein/Protein.txt")

library(proteoQ)
load_expts("~/proteoq_bypass/proteoQ/examples")

# Exemplary protein MDS
prnMDS(
  show_ids = FALSE, 
  width = 8,
  height = 4,
)

1.7 LFQ

See notes here.

2 Basic informatics

In this section I illustrate the following applications of proteoQ:

• Basic informatic analysis against peptide and protein data.
• Linear modeling using contrast fits

Unless otherwise mentioned, the in-function filtration of data by varargs of filter_ is available throughout this section of informatic analysis. Row ordering of data, indicated by arrange_, is available for heat map applications using pepHM, prnHM and plot_metaNMF.

2.1 MDS

We first visualize MDS and Euclidean distance against the peptide data. We start with metric MDS for peptide data (prnMDS for proteins):

dat_dir <- "~/proteoQ/examples"

# all data
pepMDS(
  show_ids = FALSE,
)

Figure 3A. MDS of peptide log2FC at scale_log2r = TRUE

It is clear that the WHIM2 and WHIM16 samples are well separated by the Euclidean distance of log2FC (Figure 3A). We next take the JHU data subset as an example to explore batch effects in the proteomic sample handling:

# `JHU` subset
pepMDS(
  col_select = JHU,
  filename = jhu.png,
  show_ids = FALSE,
  height = 3,
  width = 8,
)

Figure 3B-3C. MDS of peptide log2FC for the JHU subset. Left: original aesthetics; right, modefied aesthetics

We immediately spot that all samples are coded with the same color (Figure 3B). This is not a surprise as the values under column expt_smry.xlsx::Color are exclusively JHU for the JHU subset. For similar reasons, the two different batches of TMT1 and TMT2 are distinguished by transparency, which is governed by column expt_smry.xlsx::Alpha. We may wish to modify the aesthetics using different keys: e.g., color coding by WHIMs and size coding by batches, without the recourse of writing new R scripts. One solution is to link the attributes and sample IDs by creating additional columns in expt_smry.xlsx. In this example, we have had coincidentally prepared the column Shape and Alpha to code WHIMs and batches, respectively, for the JHU subset. Therefore, we can recycle them directly to make a new plot (Figure 3C):

# new `JHU` subset
pepMDS(
  col_select = JHU,
  col_fill = Shape, # WHIMs  
  col_size = Alpha, # batches
  filename = new_jhu.png,
  show_ids = FALSE,
  height = 3,
  width = 8,
)

While MDS approximates Euclidean and other distance measures at a low-dimensional space. Sometimes it may be useful to have an accurate view of the distance matrix. Functions pepEucDist and prnEucDist plot the heat maps of Euclidean distance matrix for peptides and proteins, respectively. Supposed that we are interested in visualizing the distance matrix for the JHU subset:

# `JHU` subset
pepEucDist(
  col_select = JHU,
  annot_cols = c("Shape", "Alpha"),
  annot_colnames = c("WHIM", "Batch"), 
  
  # `pheatmap` parameters 
  display_numbers = TRUE, 
  number_color = "grey30", 
  number_format = "%.1f",
  
  clustering_distance_rows = "euclidean", 
  clustering_distance_cols = "euclidean", 
  
  fontsize = 16, 
  fontsize_row = 20, 
  fontsize_col = 20, 
  fontsize_number = 8, 
  
  cluster_rows = TRUE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  border_color = "grey60", 
  cellwidth = 24, 
  cellheight = 24, 
  width = 14,
  height = 12, 
  
  filename = jhu.png,
)

The graphic controls of heat maps are achieved through pheatmap with modifications. Parameter annot_cols defines the tracks to be displayed on the top of distance-matrix plots. In this example, we have chosen expt_smry.xlsx::Shape and expt_smry.xlsx::Alpha, which encodes the WHIM subtypes and the batch numbers, respectively. Parameter annot_colnames allows us to rename the tracks from Shape and Alpha to WHIM and Batch, respectively, for better intuition. We can alternatively add columns WHIM and Batch if we choose not to recycle and rename columns Shape and Alpha.

Figure 3D. EucDist of peptide log2FC at scale_log2r = TRUE

The utility is currently applied to Euclidean distances with an argument adjEucDist for a probable compensation of distances between TMT experiments. As mentioned earlier, the quantitative log2FC are measured in relative to the reference materials under each multiplex TMT experiments. When concatenating data across TMT experiments, the measurement errors may accumulate differently. Likely the uncertainty in the reference signals will be greater if we were to prepare the references at an earlier stage of sample handling as opposed to a later stage. I tried to go through the most fundamental calculations step-by-step to help myself understand the differences:

Figure 3E. Accumulation of Euclidean distance in the interplex comparison of log2FC

The adjustment might be more suitable for studies where both the samples and references are largely similar in proteome compositions. The setting of adjEucDist = TRUE would discount the distances between references when using visualization techniques such a MDS or distance heat maps. In the cases that sample differences are exceedingly greater than handling errors, the setting of adjEucDist = FALSE would probably be more appropriate.

2.2 PCA

The utilities for PCA analysis are pepPCA and prnPCA for peptide and protein data, respectively. They are wrappers of the stats::prcomp. Data scaling and centering are the two aspects that have been emphasized greatly in PCA analysis. Some notes on proteoQ data scaling are available in section 3.1.1; hence in the present section, we will focus only on trials against data being scaled. Additional notes about data centering can be found here.

2.2.1 Overall settings

With proteoQ, the option in data scaling is set by variable scale_log2r, which will be passed to the scale. in stats::prcomp. For data centering, proteoQ relays the TRUE default to stats::prcomp.

2.2.2 Mean deviation

Provided the importance of data centering in PCA and several other analyses, proteoQ further incorporated the three columns of prot_mean_raw, prot_mean_n and prot_mean_z in protein outputs. The first one summarizes the mean log2FC before data alignment for individual proteins across selected samples. The second and the three compute the corresponding mean log2FC after data alignment, with and without scaling normalization, respectively (see also section 4 for column keys). The corresponding columns summarizing the mean deviation in peptide data are pep_mean_raw, pep_mean_n and pep_mean_z. As usual, the sample selections can be customized through the argument col_select.

2.2.3 Leverage points

The mean log2FC of proteins or peptides may serve as indicators that how far a given protein or peptide species is away from the data centering format (a.k.a. mean deviation form) that will be enforced by default in PCA. Taking protein data as an example, we will go through couple settings in prnPCA. At first, we performed PCA with data centering by default (Figure 4A):

prnPCA(
  col_select = Select, 
  show_ids = FALSE, 
  filename = cent.png,
)

We next performed another PCA with the removals of proteins that are far from mean deviation form (Figure 5B):

# observe that the overall deviations from "mean zero" may not be symmetric
prnPCA(
  col_select = Select, 
  show_ids = FALSE, 
  filter_prots_by = exprs(prot_mean_z >= -.25, prot_mean_z <= .3),
  filename = sub_cent.png,
)

Note that the clusterings are tightened under each sample type of W2 or W16 after the filter_prots_by filtration. Further note that the proportion of variance explained in the first principal axis decreased from 57.5% to 55.4% after the data filtration. This suggests that the entries deviating the most from mean zero are more leveraging towards the explained variance, even with data centering. In other words, high deviating entries are in general associated with above-average data variance, in relative to the entire data set. The observation also indicates that a high value of proportion of variance explained may not necessary be a go-to standard for differentiating sample types in that variance may be sensitive to leveraging data points.

Figure 4A-4B. PCA of protein log2FC with data centering on. Left: without filtration; right, with filtration

We next explore the analogous, but by turning off data centering:

prnPCA(
  col_select = Select, 
  center = FALSE,
  show_ids = FALSE, 
  filename = nocent.png,
)

prnPCA(
  col_select = Select, 
  center = FALSE,
  show_ids = FALSE, 
  filter_prots_by = exprs(prot_mean_z >= -.25, prot_mean_z <= .3),
  filename = sub_nocent.png,
)

First note that there is no labels of the proportion of variance explained since such a view of variance is often not suitable without data centering. Instead, an interpretation as square Euclidean distance would be more appropriate.

Further note the wider spread in PC1 and narrower in PC2 for the analysis without the removal of high deviation entries (Figure 4C versus 4D). The driving force for the difference may be again ascribed to the more leveraging data entries. Intuitively speaking, the high leverage points tend to associate with higher-than-normal Euclidean distance. This becomes more evident after the removals of the high deviation entries (Figure 4D).

The above showcases that the choice in data centering can lead to different interpretation in biology, which may be in part ascribed to high deviation entries. The phenomena can, however, be conveniently explored via proteoQ.

Figure 4C-4D. PCA of protein log2FC. Left: data centering off without filtration; right, data centering off with filtration

2.2.4 Graphic controls

The y-labels in Figure 4C are not well separated. This can be fixed by providing a custom theme to prnPCA (see also the help document via ?prnPCA). Alternatively, we may export the PCA results for direct ggplot2:

res <- prnPCA(
  col_select = Select, 
  center = FALSE,
  show_ids = FALSE, 
  filename = foo.png,
)
# names(res)

library(ggplot2)

my_theme <- theme_bw() + theme(
  axis.text.x  = element_text(angle=0, vjust=0.5, size=20),
  axis.text.y  = element_text(angle=0, vjust=0.5, size=20),
  axis.title.x = element_text(colour="black", size=20),
  axis.title.y = element_text(colour="black", size=20),
  plot.title = element_text(face="bold", colour="black", size=20, hjust=0.5, vjust=0.5),
  
  panel.grid.major.x = element_blank(),
  panel.grid.minor.x = element_blank(),
  panel.grid.major.y = element_blank(),
  panel.grid.minor.y = element_blank(),
  
  legend.key = element_rect(colour = NA, fill = 'transparent'),
  legend.background = element_rect(colour = NA,  fill = "transparent"),
  legend.title = element_blank(),
  legend.text = element_text(colour="black", size=14),
  legend.text.align = 0,
  legend.box = NULL
)

p <- ggplot(res$pca) +
  geom_point(aes(x = PC1, y = PC2, colour = Color, shape = Shape, 
                 alpha = Alpha), size = 4, stroke = 0.02) + 
  scale_y_continuous(breaks = seq(5, 15, by = 5)) + 
  labs(title = "", x = paste0("PC1 (", res$prop_var[1], ")"), y = paste0("PC2 (", res$prop_var[2], ")")) +
  coord_fixed() + 
  my_theme

ggsave(file.path(dat_dir, "Protein/PCA/nocent_2.png"), width = 6, height = 4)

Figure 4E. Custom plot.

2.2.5 Beyond the first two dimensions

The PCA findings at higher dimensions may be visualized via pairwise plots between principal components.

prnPCA(
  show_ids = FALSE,
  rank. = 4, 
  dimension = 3,
  filename = d3.png,
)

Figure 4F. Higher dimensions.

Additional examples and analogous high-dimension MDS can be found from the help documents via ?prnPCA and ?prnMDS, respectively.

2.3 LDA

See notes here.

2.4 Correlation plots

In this section, we visualize the batch effects and biological differences through correlation plots. The proteoQ tool currently limits itself to a maximum of 44 samples for a correlation plot. In the document, we will perform correlation analysis against the PNNL data subset. By default, samples will be arranged by the alphabetical order for entries under the column expt_smry.xlsx::Select. We have learned from the earlier MDS analysis that the batch effects are smaller than the differences between W2 and W16. We may wish to put the TMT1 and TMT2 groups adjacent to each other for visualization of more nuance batch effects, followed by the comparison of WHIM subtypes. We can achieve this by supervising sample IDs at a customized order. In the expt_smry.xlsx, We have prepared an Order column where samples within the JHU subset were arranged in the descending order of W2.TMT1, W2.TMT2, W16.TMT1 and W16.TMT2. Now we tell the program to look for the Order column for sample arrangement:

# peptide logFC
pepCorr_logFC(
  col_select = PNNL,
  col_order = Order, 
  filename = pep_pnnl.png,
)

# protein logFC
prnCorr_logFC(
  col_select = PNNL,
  col_order = Group,
  filename = prn_pnnl.png,
)

Figure 5A-5B. Correlation of log2FC for the PNNL subset. Left: peptide; right, protein

To visualize the correlation of intensity data, we can use pepCorr_logInt and prnCorr_logInt for peptide and protein data, respectively. More details can be assessed via ?pepCorr_logFC.

2.5 Heat maps

Heat map visualization is commonly applied in data sciences. The corresponding facilities in proteoQ are pepHM and prnHM for peptide and protein data, respectively. They are wrappers of pheatmap with modifications and exception handlings. More details can be found by accessing the help document via ?prnHM.

The following shows an example of protein heat map:

prnHM(
  xmin = -1, 
  xmax = 1, 
  xmargin = 0.1, 
  annot_cols = c("Group", "Color", "Alpha", "Shape"), 
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"), 
  cluster_rows = TRUE, 
  cutree_rows = 10, 
  show_rownames = FALSE, 
  show_colnames = TRUE, 
  fontsize_row = 3, 
  cellwidth = 14, 
  filter_sp = exprs(species == "human"),
)

we chose to top annotate the heat map with the metadata that can be found under the columns of Group, Color, Alpha and Shape in expt_smary.xlsx. For better convention, we rename them to Group, Lab, Batch and WHIM to reflect their sample characteristics. We further supplied a vararg of filter_sp where we assume exclusive interests in human proteins.

Figure 6A. Heat map visualization of protein log2FC

Row ordering of data is also implemented in the heat map utility.

prnHM(
  xmin = -1, 
  xmax = 1, 
  xmargin = 0.1, 
  annot_cols = c("Group", "Color", "Alpha", "Shape"), 
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"), 
  cluster_rows = FALSE, 
  annot_rows = c("kin_class"), 
  show_rownames = TRUE, 
  show_colnames = TRUE, 
  fontsize_row = 2, 
  cellheight = 2, 
  cellwidth = 14, 
  filter_kin = exprs(kin_attr == TRUE, species == "human"),
  arrange_kin = exprs(kin_order, gene),
  filename = "hukin_by_class.png", 
)

In the above example, we applied vararg filter_kin to subset human kinases from the protein data set by values under its kin_attr and the species columns. We further row annotate the heat map with argument annot_rows, which will look for values under the kin_class column. With the vararg, arrange_kin, we supervise the row ordering of kinases by values under the kin_order column and then those under the gene column. Analogous to the user-supplied filter_ arguments, the row ordering varargs need to start with arrange_ to indicate the task of row ordering.

Figure 6B. Heat map visualization of kinase log2FC

See ?standPep for peptide examples.

2.6 Significance tests

In this section, we perform the significance analysis of peptide and protein data. The approach of contrast fit (Chambers, J. M. Linear models, 1992; Gordon Smyth et al., limma) is taken in proteoQ. We will first define the contrast groups for significance tests. For this purpose, I have devided the samples by their WHIM subtypes, laboratory locations and batch numbers. This ends up with entries of W2.BI.TMT1, W2.BI.TMT2 etc. under the expt_smry.xlsx::Term column. The interactive environment between the Excel file and the proteoQ tool allows us to enter more columns of contrasts when needed. For instance, we might also be interested in a more course comparison of inter-laboratory differences without batch effects. The corresponding contrasts of W2.BI, W16.BI etc. can be found under a pre-made column, Term_2. Having these columns in hand, we next perform significance tests and data visualization for peptide and protein data:

# significance tests
pepSig(
  impute_na = FALSE, 
  W2_bat = ~ Term["W2.BI.TMT2-W2.BI.TMT1", 
                  "W2.JHU.TMT2-W2.JHU.TMT1", 
                  "W2.PNNL.TMT2-W2.PNNL.TMT1"], # batches
  W2_loc = ~ Term_2["W2.BI-W2.JHU", 
                    "W2.BI-W2.PNNL", 
                    "W2.JHU-W2.PNNL"], # locations
  W16_vs_W2 = ~ Term_3["W16-W2"], # types
)

# formulas matched to pepSig
prnSig(impute_na = FALSE)

# volcano plots
pepVol()
prnVol()

Note that we have informed the pepSig and prnSig utility to look for contrasts under columns Term, Term_2 etc., followed by the cotrast pairs in square brackets. Pairs of contrasts are separated by commas. The option of impute_na was set to FALSE as we might not known yet to impute NA values or not. For more examples, such as at impute_na = TRUE, try ?prnSig.

The pepVol and prnVol utility will by default match the formulas of contrasts with those in pepSig. The following plots show the batch difference between two TMT experiments for each of the three laboratories and the location difference between any two laboratories.

Figure 7A-7B. Volcano plots of protein log2FC. Left: between batches; right: between locations.

In general, the special characters of + and - in contrast terms need to be avoided in linear modeling. However, it may be sporadically convenient to use A+B to denote a combined treatment of both A and B. In the case, we will put the term(s) containing + or - into a pair of pointy brackets. The syntax in the following hypothetical example will compare the effects of A, B, A+B and the average of A and B to control C.

# note that <A + B> is one condition whereas (A + B) contains two conditions
prnSig(
  fml = ~ Term["A - C", "B - C", "<A + B> - C", "(A + B)/2 - C"],
)

In addition to the fixed effects shown above, significance tests with additive random effects are also supported. More examples can be found via ?prnSig and Lab 3.3 in the document.

2.7 Gene sets under volcano plots

There are a handful of R tools for gene set enrichement analysis, such as GSEA, GSVA, gage, to name a few. It may be intuitive as well if we can analyze and visualize the enrichment of gene sets under the context of volcano plots at given contrasts. Provided the richness of R utilities in linear modelings, the preoteoQ takes a naive approach thereafter to assess the asymmetricity of protein probability \(p\) values under volcano plots.

In the analysis of Gene Set Probability Asymmetricity (GSPA), protein significance \(p\) values from linear modeling are first taken and separated into the groups of up or down expressed proteins within a gene set. The default is to calculate the geometric means, \(P\), for each of the two groups with a penalty-like term:

\[-log10(P)=(\sum_{i=1}^{n}-log10(p_{i})+m)/(n+m)\]

where \(n\) and \(m\) are the numbers of entries with \(p\) values \(\le\) or less than a significance cut-off, respectively, under a gene set. The quotient of the two \(P\) values, one for up and one for down, is then taken to represent the significance of enrichment for a given gene set. Alternatively, the significance can be assessed via moderated t-test between the two groups. With either method, the corresponding mean log2FC are each calculated for the ups and the downs where the difference is used as the fold change of enrichment.

At the input levels, the arguments pval_cutoff and logFC_cutoff allow us to set aside low impact genes, for instance, (re)distributing them between the \(n\)-entry significance group and the \(m\)-entry insignificance group. On the output levels, argument gspval_cutoff sets a threshold in gene set significance for reporting. More details can be found from the help document via ?prnGSPA. Note that currently there is no peptide counterpart for the enrichment analysis.

We began with the analysis of GSPA against enrichment terms defined in gene ontology (GO) and molecular signatures (MSig) data sets:

prnGSPA(
  impute_na = FALSE,
  pval_cutoff = 5E-2, # protein pVal threshold
  logFC_cutoff = log2(1.2), # protein log2FC threshold
  gspval_cutoff = 5E-2, # gene-set threshold
  gslogFC_cutoff = log2(1.2), # gene-set log2FC threshold
  gset_nms = c("go_sets", "c2_msig"), 
)

The formulas of contrasts will by default match to the those used in pepSig. The species will be determined automatically from input data and the corresponding databases will be loaded. In the above example of pdx, databases of GO and MSig will be loaded for both human and mouse. If we choose to focus on human proteins, we can add a vararg statement such as filter_sp = exprs(species == "human").

We next visualize the distribution of protein log2FC and pVal within gene sets:

gspaMap(
  show_labels = TRUE,
  gspval_cutoff = 5E-3, 
  gslogFC_cutoff = log2(1.2), 
  # topn = 100, 
  gset_nms = c("go_sets"),
  show_sig = pVal,
  xco = 1.2, # position of two vertical lines for FC
  yco = 0.05, # position of a horizental line for pVal
)

This will produce the volcano plots of proteins under gene sets that have passed our selection criteria. Here, we show one of the examples:

Figure 8A. An example of volcano plots of protein log2FC under a gene set. Top, method = mean; bottom, method = limma.

The gene sets of GO and MSig are availble for species human, mouse and rat in proteoQ. For custom gene sets and/or additional species, the utility prepGO will download and prepare GO data according to custom-supplied URLs. In the follow examples, we prepare the GO data of go_hs.rds and go_mm.rds for human and mouse, respectively, under the file folder ~\\proteoQ\\dbs\\go:

prepGO(
  species = human,
  db_path = "~/proteoQ/dbs/go",
  gaf_url = "http://current.geneontology.org/annotations/goa_human.gaf.gz",
  obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
  filename = go_hs.rds,
)

prepGO(
  species = mouse,
  db_path = "~/proteoQ/dbs/go",
  gaf_url = "http://current.geneontology.org/annotations/mgi.gaf.gz",
  obo_url = "http://purl.obolibrary.org/obo/go/go-basic.obo",
  filename = go_mm.rds,
)

# head(readRDS(file.path("~/proteoQ/dbs/go", "go_hs.rds")))
# head(readRDS(file.path("~/proteoQ/dbs/go", "go_mm.rds")))

Similarly, we prepare custom MSig data bases for human and mouse:

prepMSig(
  # msig_url = "https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.0/c2.all.v7.0.entrez.gmt",
  # db_path = "~/proteoQ/dbs/msig",
  species = human,
  filename = msig_hs.rds,
)

prepMSig(
  # msig_url = "https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.0/c2.all.v7.0.entrez.gmt",
  # ortho_mart = mmusculus_gene_ensembl, 
  # db_path = "~/proteoQ/dbs/msig",  
  species = mouse,
  filename = msig_mm.rds,
)

# head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_hs.rds")))
# head(readRDS(file.path("~/proteoQ/dbs/msig", "msig_mm.rds")))

We need to provide the list name of ortho_mart for species other than human, mouse and rat. The value will be used for ortholog lookups via biomaRt. More details are available in the help document via ?prepMSig. Note that the data bases will be stored as .rds files, which can be used with prnGSPA and gspaMap for analysis and visualization:

# start over
unlink(file.path(dat_dir, "Protein/GSPA"), recursive = TRUE, force = TRUE)

prnGSPA(
  impute_na = FALSE,
  pval_cutoff = 5E-2,
  logFC_cutoff = log2(1.2),
  gspval_cutoff = 5E-2,
  gslogFC_cutoff = log2(1.2), 
  gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
               "~/proteoQ/dbs/go/go_mm.rds", 
               "~/proteoQ/dbs/msig/msig_hs.rds", 
               "~/proteoQ/dbs/msig/msig_mm.rds"),
)

gspaMap(
  gset_nms = c("~/proteoQ/dbs/go/go_hs.rds",
               "~/proteoQ/dbs/go/go_mm.rds", 
               "~/proteoQ/dbs/msig/msig_hs.rds", 
               "~/proteoQ/dbs/msig/msig_mm.rds"),
  impute_na = FALSE,
  show_labels = FALSE, 
  gspval_cutoff = 5E-2, 
  gslogFC_cutoff = log2(1.2), 
  show_sig = pVal, 
  xco = 1.2, 
  yco = 0.05, 
)

As expected, in the examples of MSig, some breast cancer signatures in basal and luminal subtypes were captured.

Figure 8B. Examples of volcano plots of protein log2FC under molecular signatures.

Currently, proteoQ does not keep track of the values of gset_nms in the various calls to prnGSPA. When mapping the findings from prnGSPA to gspaMap, we need to be responsible for the completeness of the gene-set space. If we were to leave out the setting of gset_nms, the default of gset_nms = c("go_sets", "c2_msig") will be applied when executing gspaMap. We might thus encounter some discrepancies in the volcano plots of GO terms due to probable differences between the default and the custom data bases.

For simplicity, it is generally applicable to include all the data bases that have been applied to prnGSPA in a custom workflow and, in that way, no terms will be missed out for visualization. This is also suitable in that gspaMap merely perform volcano plot visualization by gene sets and no multiple-test correlations are involved.

In addition to finding gene sets with significance, prnGSPA reports the essential gene sets using a greedy set cover algorithm by RcppGreedySetCover. The correspondance between essential and all of the gene sets are stored in _essmap.txt files under the Protein\GSPA folder.

The utility in proteoQ for conventional GSEA analysis is prnGSEA(). Gene set variance analysis (GSVA) is available through prnGSVA. Details can be found via ?prnGSEA and ?prnGSVA, respectively, from an R console.

2.8 Gene set networks

In the above section, we have plotted the enrichment of gene sets by individual GO or KEGG terms. Depending on how much the sample groups contrast to each other, we could have produced more plots where many of them might never get viewed. Besides, gene sets can be redundant with overlaps to one another to varying degrees. A means to communicate the gene set results at high levels is to present them as hierarchical trees or grouped networks.

In this section, we will visualize the connectivity of significant gene sets by both distance heat maps and networks. For simplicity, the heat maps or networks will be constructed only between gene sets and essential gene sets. As mentioned in section Gene sets under volcano plots, the essential gene sets were approximated with greedy set cover. This will reduce the dimensionality of data from \(n \times n\) to \(n \times m\) (\(m \le n\)).

We next gauge the redundancy of a gene set in relative to an essential set by counting the numbers of intersecting gene IDs. This is documented as the fraction of overlap between gene sets when calling prnGSPA. The values are available in output files such as Protein\GSPA\essmap_.*.csv. For network visualization, the gene sets are further classified by their distance using hierarchical clustering.

In this following, we first perform simple heat map visualization between all significant gene sets in columns and essential groups in rows.

prnGSPAHM(
  annot_cols = "ess_idx",
  annot_colnames = "Eset index",
  filename = "all_sets.png", 
)

The distance in heat is \(D = 1-f\) where \(f\) is the fraction of overlap in IDs between two gene sets. The smaller the distance, the greater the overlap is between two gene sets. For convenience, a distance column is also made available in the _essmap.txt file.

Figure 8C. Heat map visualization of the distance between all and essential gene sets. The contrasts are defined in ‘prnSig(W2_loc = )’ in section 2.4 Significance tests and volcano plot visualization

As expected, we saw zero overlap between human and mouse gene sets. Within each organism, low-redundancy red cells overwhelm the heat map and might have impeded us from capturing high-redundancy terms in blue. We can, however, readily de-emphasize the red cells by data filtration. In the example shown below, we chose to keep more redundant terms at distances shorter than or equal to 0.33:

prnGSPAHM(
  filter2_by = exprs(distance <= .33),
  filter2_sp = exprs(start_with_str("hs", term)), 
  annot_cols = "ess_idx",
  annot_colnames = "Eset index",
  annot_rows = "ess_size", 
  filename = show_human_redundancy.png,
)

Note that there is a second vararg expression, exprs(start_with_str("hs", term)). In this expression, we have used a pseudonym approach to subset terms starting with character string hs under the column term in GSPA result files, which corresponds to human gene sets for both GO and KEGG.¹³ More examples of the pseudonym approach can be found from Lab 3.2 in this document. More examples of the utility can be found via ?prnGSPAHM.

Figure 8D. Heat map visualization of human gene sets at a distance cut-off 0.2

Aside from heat maps, prnGSPAHM produces the networks of gene sets via networkD3, for interactive exploration of gene set redundancy.

Figure 8E. Snapshots of the networks of biological terms. Left, distance <= 0.8; right, distance <= 0.2.

2.9 Trend Analysis

In this section, we perform the trend analysis against protein expressions. More information can be found from cmeans.

2.8.1 Clustering

The utility for the clustering of protein log2FC is anal_prnTrend. Note that the number of clusters is provided by n_clust, which can be a single value or a vector of integers.

anal_prnTrend(
  n_clust = c(5:6), 
  filter_by_npep = exprs(prot_n_pep >= 2),
)

The above codes will generate result files, Protein_Trend_Z_nclust5.txt and Protein_Trend_Z_nclust6.txt, under the ...\Protein\Trend directory. The letter Z in the file names remind us that the results were derived from normalized protein data with the option of scale_log2r = TRUE. More details are available via ?anal_prnTrend from a R section.

2.8.2 Visualization

We next visualize the results:

plot_prnTrend(
  col_order = Order,
)

The argument col_order provides a means to supervise the order of samples during the trend visualization. In the above example, the plot_prnTrend will look into the field under the expt_smry.xlsx::Order column for sample arrangement (see also Section 2.3 Correlation plots).

Figure 9A. Trends of protein log2FC (n_clust = 6).

We can subset the secondary input data by filter2_ varargs. In the example shown below, we choose to visualize only the pattern of trends in cluster 4. Note that cluster is a column key in Protein_Trend_[...].txt:

plot_prnTrend(
  col_order = Order,
  filter2_by = exprs(cluster == 4),
  width = 12, 
  height = 12,
  filename = cl4.png,
)

Figure 9B. Trends of protein log2FC at cluster 4 (n_clust = 6).

We can also select certain sample groups for visualization, for instance, the samples under the column of expt_smry.xlsx::BI:

plot_prnTrend(
  col_order = Order, 
  col_select = BI,
  filename = bi.png,
)

Figure 9C. Trends of protein log2FC for BI subset (n_clust = 6).

Note the difference between

anal_prnTrend(col_select = BI, ...)
plot_prnTrend(col_select = NULL, ...)

and

anal_prnTrend(col_select = NULL, ...)
plot_prnTrend(col_select = BI, ...)

Apparently, they will both plot the trends of protein log2FC for the BI subset. In spite, the former is based on the clustering results from the BI subset whereas the later is based on the findings from all samples. The same consideration will typically hold for various informatic analysis in proteoQ, including the NMF analysis that we will next discuss.

2.8.3 API

The trend findings from anal_prnTrend can be loaded automatically to the ClueGO utility in Cytoscape. The installation of yFiles Layout Algorithms is also required.

# Make sure that Cytoscape is open
cluego(
  df2 = Protein_Trend_Z_nclust5.txt, 
  species = c(human = "Homo Sapiens"), 
  n_clust = c(3, 5)
)

Note that human is a value that can be found under the column species in Protein_Trend_Z_nclust5.txt and Homo Sapiens is the corresponding name used in ClueGO.

2.10 NMF Analysis

In this section, we will performs the analysis of non-negative matrix factorization (NMF) against protein data. More details can be found from NMF and the ?anal_prnNMF wrapper. Since additional arguments can be passed on to NMF, we will test below protein classifications with both the default and the ‘lee’ method:

# load library
library(NMF)

# NMF analysis
anal_prnNMF(
  impute_na = FALSE,
  col_group = Group, # optional a priori knowledge of sample groups
  r = c(5:6),
  nrun = 20, 
  seed = 123,
  filter_by_npep = exprs(prot_n_pep >= 2),
)

anal_prnNMF(
  impute_na = FALSE,
  col_group = Group,
  method = "lee",
  r = c(5:6),
  nrun = 20, 
  seed = 123,
  filter_by_npep = exprs(prot_n_pep >= 2),
  filename = lee.txt,
)

Analogous analysis for peptide data are available via anal_pepNMF(...).

Following the primary NMF analysis, secondary utilities of plot_pepNMFCon and plot_prnNMFCon prepare the consensus heat maps of peptide and protein data, respectively. Similarly, plot_pepNMFCoef and plot_prnNMFCoef prepare coefficient heat maps. Utility plot_metaNMF makes the heat maps of protein log2FC. These utilities can pass arguments to pheatmap as shown in Section 2.3. In the examples shown below, we plot the heat maps for protein data against all available ranks, which are 5 and 6, specified earlierly in the anal_prnNMF step.

plot_prnNMFCon(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  width = 14,
  height = 14, 
)

plot_prnNMFCoef(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"), 
  annot_colnames = c("Lab", "Batch", "WHIM"), 
  width = 14, 
  height = 6, 
)

plot_metaNMF(
  impute_na = FALSE,
  annot_cols = c("Color", "Alpha", "Shape"), 
  annot_colnames = c("Lab", "Batch", "WHIM"), 
  cell_width = 6,
  fontsize = 6, 
  fontsize_col = 5,
)

Argument impute_na reminds us which piece(s) of NMF results from the corresponding anal_[...]NMF will be used for plotting. The same is true for scale_log2r, which defaults at TRUE. An error message will be noted if no corresponding analysis results were found.

Visualization aganist data subset is also feasible. In the next example, we will prepare heat maps for samples under column BI in expt_smry.xlsx. We further limit ourselves to results from anal_prnNMF at r = 5. In metagene plots, we choose additionally to row order data by genes via the arrange_ vararg:

plot_prnNMFCon(
  impute_na = FALSE,
  col_select = BI,
  r = 5, 
  annot_cols = c("Color", "Alpha", "Shape"),
  annot_colnames = c("Lab", "Batch", "WHIM"),
  fontsize = 8, 
  fontsize_col = 6,
  fontsize_row = 6, 
  width = 6.5,
  height = 6, 
  filename = bi_r5_con.png, 
)

plot_prnNMFCoef(
  impute_na = FALSE,
  col_select = BI,
  r = 5, 
  annot_cols = c("Color", "Alpha", "Shape"), 
  annot_colnames = c("Lab", "Batch", "WHIM"), 
  fontsize = 8, 
  fontsize_col = 6,
  fontsize_row = 6, 
  width = 12,
  height = 3, 
  filename = bi_r5_coef.png, 
)

plot_metaNMF(
  impute_na = FALSE,
  col_select = BI,
  r = 5, 
  annot_cols = c("Color", "Alpha", "Shape"), 
  annot_colnames = c("Lab", "Batch", "WHIM"), 
  # fontsize = 5, 
  # fontsize_col = 5,
  # cellwidth = 6,
  # cellheight = 6,
  cluster_rows = FALSE,
  arrange_by = exprs(gene),   
  filename = bi_r5_rowordered.png,
)

The silhouette information was obtained via the R package cluster and shown as a track on the top of consensus and coefficient heat maps.

Figure 10A-10B. Heat map visualization of protein NMF results with default method (results from method = “lee” not shown). Left: concensus; right: coefficients; metagenes not shown.

While utility plot_prnTrend in trend visualization (Section 2.7) can take a customized theme for uses in ggplot2 therein, the plot_ functions in NMF are wrappers of pheatmap and thus can process a user-supplied color palette.

plot_prnNMFCon(
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(50), 
  ...
)

plot_prnNMFCoef(
  color = ..., 
)

plot_metaNMF(
  color = ...,
)

2.11 STRING Analysis

The following performs the STRING analysis of protein-protein interactions. More details can be found from ?anal_prnString.

anal_prnString(
  db_path = "~/proteoQ/dbs/string",
  score_cutoff = .9,
  filter_by_sp = exprs(species %in% c("human", "mouse")), 
  filter_prots_by = exprs(prot_n_pep >= 2),
)

The results of protein-protein interaction is summarised in Protein_String_[...]_ppi.tsv and the expression data in Protein_String_[...]_expr.tsv. The files are formatted for direct applications with Cytoscape. When calling anal_prnString, the corresponding databases will be downloaded automatically if not yet present locally. One can also choose to download separately the databases for a given species:

dl_stringdbs(
  species = rat,
  db_path = "~/proteoQ/dbs/string", 
)

2.12 Missing value imputation

Imputation of peptide and protein data are handle with pepImp and prnImp. More information can be found from mice and ?prnImp.

3 Labs

3.1 Reference choices

In this lab, we explore the effects of reference choices on data normalization and cleanup.

3.1.1 References on data scaling

We first copy data over to the file directory specified by temp_dir, followed by PSM, peptide normalization and histogram visualization of peptide log2FC.

# exemplary data
temp_dir <- "~/proteoQ/ref_w2"
dir.create(temp_dir, recursive = TRUE, showWarnings = FALSE)

library(proteoQDA)
copy_global_mascot(temp_dir)
copy_w2ref_exptsmry(temp_dir)
copy_global_fracsmry(temp_dir)

# analysis
library(proteoQ)
load_expts(temp_dir, expt_smry_ref_w2.xlsx)

normPSM(
  group_psm_by = pep_seq,
  group_pep_by = gene, 
  fasta = c("~/proteoQ/dbs/fasta/refseq/refseq_hs_2013_07.fasta", 
            "~/proteoQ/dbs/fasta/refseq/refseq_mm_2013_07.fasta"), 
  rptr_intco = 1000,
  rm_craps = TRUE,
  rm_krts = FALSE,
  rm_outliers = FALSE, 
  annot_kinases = TRUE, 
  plot_rptr_int = TRUE, 
  plot_log2FC_cv = TRUE, 
  filter_peps = exprs(pep_expect <= .1), 
)

PSM2Pep()
mergePep()
standPep()

pepHist(
  scale_log2r = FALSE, 
  ncol = 9,
)

Notice that in the histograms the log2FC profiles of WHIM16 samples are much narrower than those of WHIM2 (Figure S1A). This will occur when a reference is more similar to one group of sample(s) than the other. In our case, the reference is one of WHIM2. The difference in the breadth of log2FC profiles between the WHIM16 and the WHIM2 groups is likely due to the genuine difference in their proteomes. If the above argument is valid, a scaling normalize would moderate, and thus bias, the quantitative difference in proteomes between WHIM2 and WHIM16.

Figure S1A. Histograms of peptide log2FC with a WHIM2 reference.

We alternatively seek a “center-of-mass” representation for uses as references. We select one WHIM2 and one WHIM16 from each 10-plex TMT. The proteoQ tool will average the signals from designated references. Thefore, the derived reference can be viewed as a mid point of the WHIM2 and the WHIM16 proteomes. We next perform analogously the data summary and histogram visualization.

temp_dir_w2w16 <- "~/proteoQ/ref_w2w16"
dir.create(temp_dir_w2w16, recursive = TRUE, showWarnings = FALSE)

library(proteoQDA)
copy_global_mascot(temp_dir_w2w16)
copy_w2w16ref_exptsmry(temp_dir_w2w16)
copy_global_fracsmry(temp_dir_w2w16)

library(proteoQ)
load_expts(temp_dir_w2w16, expt_smry_ref_w2_w16.xlsx)

normPSM(
  group_psm_by = pep_seq,
  group_pep_by = gene, 
  fasta = c("~/proteoQ/dbs/fasta/refseq/refseq_hs_2013_07.fasta", 
            "~/proteoQ/dbs/fasta/refseq/refseq_mm_2013_07.fasta"), 
  rptr_intco = 1000,
  rm_craps = TRUE,
  rm_krts = FALSE,
  rm_outliers = FALSE, 
  annot_kinases = TRUE, 
  plot_rptr_int = TRUE, 
  plot_log2FC_cv = TRUE, 
  filter_peps = exprs(pep_expect <= .1), 
)

PSM2Pep()
mergePep()
standPep()

pepHist(
  scale_log2r = FALSE, 
  ncol = 8,
)

With the new reference, we have achieved log2FC profiles that are more comparable in breadth between WHIM2 and WHIM16 samples and a subsequent scaling normalization seems more suitable.

Figure S1B. Histograms of peptide log2FC with a combined WHIM2 and WHIM16 reference.

3.1.2 References on data CV

In this section, we explore the effects of reference choices on the CV of log2FC. For simplicity, we will visualize the peptide data that link to the BI subset at batch number one. We first add a new column, let’s say BI_1, in expt_smry_ref_w2.xlsx with the corresponding samples being indicated. We next display the distributions of proteins CV measured from contributing peptides before data removals (Figure S1C):

# continue on the `ref_w2` example in section 3.1.1
library(proteoQ)
load_expts("~/proteoQ/ref_w2", expt_smry_ref_w2.xlsx, frac_smry.xlsx)

# `BI_1` subset for visualization
purgePep(
  col_select = BI_1, 
  ymax = 1.2,
  ybreaks = .5,
  width = 8,
  height = 8,
  flip_coord = TRUE, 
  filename = bi1.png,
)

Notice that the CV distributions of WHIM2 are much narrower than those of WHIM16. This makes intuitive sense given that the log2FC profiles of WHIM2 are much narrows as well (Figure S1A). To discount the genuine difference in sample CV, we next trim relatively the data points by percentiles:

purgePep(
  col_select = BI_1, 
  pt_cv = .95, 
  ymax = 1.2,
  ybreaks = .5,
  width = 8,
  height = 8,
  flip_coord = TRUE, 
  filename = bi1_ptcv.png,  
)

Figure S1C-S1D. Protein CV from peptide measures with WHIM2 reference. Left: before trimming; right: after trimming.

3.2 Data subsets and additions

The row filtrations and column additions of data are both available in proteoQ.

3.2.1 Subsets

In this lab, we will first apply pseudoname approaches to subset data. The availble pesudonames include

• contain_str: contain a literal string; “PEPTIDES” contain_str “TIDE”.
  
• contain_chars_in: contain some of the characters in a literal string; “PEPTIDES” contain_chars_in “XP”.
  
• not_contain_str: not contain a literal string; “PEPTIDES” not_contain_str “TED”.
• not_contain_chars_in: not contain any of the characters in a literal string; “PEPTIDES” not_contain_chars_in “CAB”.
  
• start_with_str: start with a literal string. “PEPTIDES” start_with_str “PEP”.
• end_with_str: end with a literal string. “PEPTIDES” end_with_str “TIDES”.
  
• start_with_chars_in: start with one of the characters in a literal string. “PEPTIDES” start_with_chars_in “XP”.
  
• ends_with_chars_in: end with one of the characters in a literal string. “PEPTIDES” ends_with_chars_in “XS”.

These functions are typically coupled to the varargs of filter_ or slice_ for the subsetting of data rows based on their names. More information can be found from the help document via ?contain_str. In the following example, we will apply contain_chars_in to subset peptide data.

The CPTAC publication contains both global and phosphopeptide data from the same samples. This allows us to explore the stoichiometry of phosphopeptide subsets in relative to the combined data sets of global + phospho peptides. We first copy over both the global and the phospho data sets to the file directory specified by dat_dir, followed by PSM, peptide normalization and histogram visualization of peptide log2FC of the BI_1 subset.

# exemplary data
dat_dir <- "~/proteoQ/phospho_stoichiometry"
dir.create(dat_dir, recursive = TRUE, showWarnings = FALSE)

library(proteoQDA)
copy_global_mascot(dat_dir)
copy_phospho_mascot(dat_dir)
copy_global_exptsmry(dat_dir)
copy_cmbn_fracsmry(dat_dir)

# analysis
library(proteoQ)
load_expts()

# note that `group_psm_by = pep_seq_mod` 
normPSM(
  group_psm_by = pep_seq_mod,
  group_pep_by = gene, 
  fasta = c("~/proteoQ/dbs/fasta/refseq/refseq_hs_2013_07.fasta", 
            "~/proteoQ/dbs/fasta/refseq/refseq_mm_2013_07.fasta"), 
  filter_peps = exprs(pep_expect <= .1), 
)

PSM2Pep()
mergePep()

standPep(
    method_align = MGKernel, 
    range_log2r = c(10, 95), 
    range_int = c(5, 95), 
    n_comp = 3, 
    seed = 883, 
    maxit = 200, 
    epsilon = 1e-05, 
)

# (a) phospho subsets without y-scaling
pepHist(
  col_select = BI_1, 
  filter_peps = exprs(contain_chars_in("sty", pep_seq_mod)), 
  scale_y = FALSE, 
  ncol = 5, 
  filename = pSTY_bi1_scaley_no.png,
)

# (b) phospho subsets with y-scaling
pepHist(
  col_select = BI_1, 
  filter_peps = exprs(contain_chars_in("sty", pep_seq_mod)), 
  scale_y = TRUE, 
  ncol = 5, 
  filename = pSTY_bi1_scaley_yes.png,
)

Note that we have applied the new grammar of contain_chars_in("sty", pep_seq_mod) to extract character strings containing lower-case letters ‘s’, ‘t’ or ‘y’ under the pep_seq_mod column in Peptide.txt. This corresponds to the subsettting of peptides with phosphorylation(s) in serine, thereonine or tyrosine.¹⁴

Figure S2A-S2B. Histograms of log2FC. Left: phosphopeptides without y-axix scaling; right: phosphopeptides with y-axix scaling. The density curves are from the combined data of global + phospho.

Ideally, the profiles of the log2FC between the phospho subsets and the overall data would either align at the maximum density or perhaps offset by similar distance among replicated samples. In this example, the alignment at maximum density seems to be the case. The observation raises the possibility of measuring the stoichiometry of phosphoproteomes in relative to global data across sample types or conditions.

In addition to pseudonyms, convenience columns such as pep_mod_protntac and pep_mod_sty are made available in Peptide.txt, to indicate the property of peptide modifications of protein N-terminal acetylation and phosphorylation, respectively. We can use alternatively the column keys to subset data, for example, extracting peptides from N-terminal acetylated proteins:

# (c) N-term acetylation subsets without y-scaling
pepHist(
  col_select = BI_1, 
  scale_log2r = TRUE, 
  filter_peps = exprs(pep_mod_protntac == TRUE), 
  scale_y = FALSE, 
  ncol = 5, 
  filename = bi1_nac_scaley_no.png,
)

# (d) N-term acetylation subsets with y-scaling
pepHist(
  col_select = BI_1, 
  scale_log2r = TRUE, 
  filter_peps = exprs(pep_mod_protntac), 
  scale_y = TRUE, 
  ncol = 5, 
  filename = bi1_nac_scaley_yes.png,
)

Figure S2C-S2D. Histograms of the log2FC of peptides from N-terminal acetylated proteins. Left: without y-axix scaling; right: with y-axix scaling.

Pseudonyms and convenience columns can be used interexchangeably for simple conditions. In the following example, we assume that peptide sequences are under the column pep_seq_mod in Peptide.txt with variably modified residues in lower case. we can exclude oxidized methione or deamidated asparagine from uses in data normalization:

Pep2Prn(
  filter_by_mn = exprs(not_contain_chars_in("mn", pep_seq_mod)),
)

standPrn(
  method_align = MGKernel, 
  range_log2r = c(5, 95), 
  range_int = c(5, 95), 
  n_comp = 2, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
)

prnHist(
  col_select = BI_1, 
  scale_log2r = TRUE, 
  scale_y = FALSE, 
  ncol = 5, 
  filter_prns_by = exprs(species == "mouse"),
  filename = "bi1_nac_scaley_no.png",
)

prnHist(
  col_select = BI_1, 
  scale_log2r = TRUE, 
  scale_y = TRUE, 
  ncol = 5, 
  filter_prns_by = exprs(species == "mouse"),
  filename = "bi1_nac_scaley_yes.png",
)

or use alternatively the convenience columns, pep_mod_m and pep_mod_n, for the same purpose:

Pep2Prn(
  filter_by_mn = exprs(pep_mod_m == FALSE, pep_mod_n == FALSE),
)

standPrn(
  method_align = MGKernel, 
  range_log2r = c(5, 95), 
  range_int = c(5, 95), 
  n_comp = 2, 
  seed = 749662, 
  maxit = 200, 
  epsilon = 1e-05, 
)

3.2.2 Column additions

Customer supplied columns can be further taken by proteoQ for various data processing and informatic analyses. In this section, we will first add a column, n_not_na, to protein table Protein.txt. The column summarizes the number of log2FCs that are NOT missing for each protein. The newly added column will then be applied to data-row filtration during heat map visualization.

# add a column to "Protein.txt"
df <- readr::read_tsv(file.path(dat_dir, "Protein/Protein.txt")) 

library(magrittr)
n_not_na <- df %>% 
  dplyr::select(grep("Z_log2_R", names(.))) %>% 
  dplyr::select(-grep("\\(Ref|\\(Empty", names(.))) %>% 
  is.na() %>% 
  `!`() %>% 
  rowSums()

df %>% 
  dplyr::mutate(n_not_na = n_not_na) %>% 
  # proteoQ::reorderCols2() %>% 
  readr::write_tsv(file.path(dat_dir, "Protein/Protein.txt"))

Note that there is a restriction in column additions in that the custom column(s) need to be anchored before the intensity and ratio fields for uses in downstream analyses. This is achieved behind the scene when the modified file is loaded, for example, in protein heat map visualization:

prnHM(
  df = "Protein/Protein.txt", 
  xmin = -1,
  xmax = 1,
  xmargin = 0.1,
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  cluster_rows = TRUE,
  cutree_rows = 10,
  show_rownames = FALSE,
  show_colnames = TRUE,
  fontsize_row = 3,
  cellwidth = 14,
  width = 18,
  height = 12,
  filter_prots_by_sp_npep = exprs(n_not_na <= 10),
  filename = "mostly_na_vals.png",
)

Importantly, we need to supply the file name to argument df. This is because a higher precedence will be given to Model/Protein_pVals.txt over Protein.txt. Without specifying the value of df, proteoQ will look for the n_not_na column that are indeed absent from Protein_pVals.txt.

Figure S2E. Scarce heat map.

Alternatively, we may add the custom column to Protein_pVals.txt:

df <- readr::read_tsv(file.path(dat_dir, "Protein/Model/Protein_pVals.txt")) 

n_not_na <- df %>% 
  dplyr::select(grep("Z_log2_R", names(.))) %>% 
  dplyr::select(-grep("\\(Ref|\\(Empty", names(.))) %>% 
  is.na() %>% 
  `!`() %>% 
  rowSums()

df %>% 
  dplyr::mutate(na_counts = na_counts) %>% 
  readr::write_tsv(file.path(dat_dir, "Protein/Model/Protein_pVals.txt"))

prnHM(
  df = "Protein/Model/Protein_pVals.txt", 
  xmin = -1,
  xmax = 1,
  xmargin = 0.1,
  annot_cols = c("Group", "Color", "Alpha", "Shape"),
  annot_colnames = c("Group", "Lab", "Batch", "WHIM"),
  cluster_rows = TRUE,
  cutree_rows = 10,
  show_rownames = FALSE,
  show_colnames = TRUE,
  fontsize_row = 3,
  cellwidth = 14,
  width = 18,
  height = 12,
  filter_prots_by_sp_npep = exprs(n_not_na <= 10),
  filename = "na30.png",
)

3.3 Random effects

Models that incorporate both fixed- and random-effects terms in a linear predictor expression are often termed mixed effects models.

3.3.1 Single random effect

In proteomic studies involved multiple multiplex TMT experiments, the limited multiplicity of isobaric tags requires sample parting into subgroups. Measures in log2FC are then obtained within each subgroup by comparing to common reference materials, followed by data bridging across experiments. This setup violates the independence assumption in statistical sampling as the measures of log2FC are batched by TMT experiments. In this lab, we will use the CPTAC data to test the statistical significance in protein abundance between the WHIM2 and the WHIM16 subtypes, by first taking the batch effects into account. We will use mixed-effects models to explore the random effects that were introduced by the data stitching. In case that you would like to find out more about mixed-effects models in R, I found the online tutorial a helpful resource.

We start off by (re)executing the reduced example shown in ?load_expts:

dat_dir <- "~/proteoQ/randeffs/examples"
dir.create(dat_dir, recursive = TRUE, showWarnings = FALSE)

library(proteoQDA)
copy_global_exptsmry(dat_dir)
copy_global_fracsmry(dat_dir)
copy_global_mascot(dat_dir)

library(proteoQ)
load_expts()

normPSM(
  group_psm_by = pep_seq_mod, 
  group_pep_by = gene, 
  annot_kinases = TRUE, 
  fasta = c("~/proteoQ/dbs/fasta/refseq/refseq_hs_2013_07.fasta",
            "~/proteoQ/dbs/fasta/refseq/refseq_mm_2013_07.fasta"),
)

PSM2Pep()
mergePep()
standPep()
pepHist()

Pep2Prn(use_unique_pep = TRUE)
standPrn()
prnHist()

We next carry out the signficance tests with and without random effects:

pepSig(
  impute_na = FALSE, 
  W2_vs_W16_fix = ~ Term_3["W16-W2"], # fixed effect only
  W2_vs_W16_mix = ~ Term_3["W16-W2"] + (1|TMT_Set), # one fixed and one random effects
)

prnSig(impute_na = FALSE)

# volcano plots
prnVol(impute_na = FALSE)

In the formula linked to argument W2_vs_W16_mix, the random effect (1|TMT_Set) is an addition to the fix effect Term_3["W16-W2"]. The syntax (1|TMT_Set) indicates the TMT_Set term to be parsed as a random effect. The name of the term is again a column key in expt_smry.xlsx. In this example, the TMT batches are documented under the column TMT_Set and can be applied directly to our formula.

Upon the completion of the protein significance tests, we can analyze analogously the gene set enrichment against these new formulas by calling functions prnGSPA, gspaMAP and prnGSPAHM. This results will contain random effects in enrichment analysis aganist gene sets.

3.3.2 Multiple random effects

In this section, we will test the statistical significance in protein abundance changes between the WHIM2 and the WHIM16 subtypes, by taking additively both the TMT batch effects and the laboratory effects into account. At the time of writing the document, I don’t yet know how to handle multiple random effects using limma. Alternatively, I use lmerTest to do the work.

Missing values can frequently fail random-effects modeling with more complex error structures and need additional cares. One workaround is to simply restrict ourselves to entries that are complete in cases. This would lead to a number of proteins not measurable in their statistical significance. Alternatively, we may seek to fill in missing values using techniques such as multivariate imputation.

We further note that the laboratory differences are coded under columns Color in expt_smry.xlsx. We then test the statistical difference between WHIM2 and WHIM16 against the following three models:

# impute NA
pepImp(m = 2, maxit = 2)
prnImp(m = 5, maxit = 5)

# significance tests
# really take a while; need to expedite `lm` as mentioned in "Advanced R" (Hadley Wichham, Ch. 24)
pepSig(
  impute_na = TRUE, # otherwise coerce to complete cases at multiple random effects
  method = lm,
  W2_vs_W16_fix = ~ Term_3["W16-W2"], # one fixed effect
  W2_vs_W16_mix = ~ Term_3["W16-W2"] + (1|TMT_Set), # one fixed and one random effect
  W2_vs_W16_mix_2 = ~ Term_3["W16-W2"] + (1|TMT_Set) + (1|Color), # one fixed and two random effects
)

prnSig(
  impute_na = TRUE, # otherwise coerce to complete cases at multiple random effects
  method = lm,
  W2_vs_W16_fix = ~ Term_3["W16-W2"], # one fixed effect
  W2_vs_W16_mix = ~ Term_3["W16-W2"] + (1|TMT_Set), # one fixed and one random effect
  W2_vs_W16_mix_2 = ~ Term_3["W16-W2"] + (1|TMT_Set) + (1|Color), # one fixed and two random effects
)

# correlation plots
read.csv(file.path(dat_dir, "Protein/Model/Protein_pVals.txt"), 
         check.names = FALSE, header = TRUE, sep = "\t") %>%
  dplyr::select(grep("pVal\\s+", names(.))) %>% 
  `colnames<-`(c("none", "one", "two")) %>% 
  dplyr::mutate_all(~ -log10(.x)) %>% 
  GGally::ggpairs(columnLabels = as.character(names(.)), labeller = label_wrap_gen(10), title = "", 
    xlab = expression("pVal ("*-log[10]*")"), ylab = expression("pVal ("*-log[10]*")")) 

The correlation plots indicate that the random effects of batches and laboratory locations are much smaller than the fixed effect of the biological differences of WHIM2 and WHIM16.

Figure S3. Pearson r of protein significance p-values.

4 Column keys

4.1 Mascot

The results are reported at the levels of PSMs, peptides and proteins. The order of column keys can vary slightly provided different databases or accession types.

4.1.1 PSMs

PSMs are reported at the basis of per TMT experiment per series of LC/MS data acquisition. The names of the result files are TMTset1_LCMSinj1_PSM_N.txt, TMTset2_LCMSinj1_PSM_N.txt et al. with the indexes of TMT experiment and LC/MS injection index being indicated in the names. The column keys are described in Matrix Science with the following additions or modifications:

Header	Descrption	Note
prot_hit_num	Ordinal number of the protein hit (or protein family when grouping enabled)	Mascot	NA
prot_family_member	Ordinal number of the protein family member when grouping enabled	Mascot	NA
prot_acc	Protein accession string	Mascot	NA
prot_desc	Protein description taken from Fasta title line	Mascot	NA
prot_score	Protein Mascot score	Mascot	NA
prot_mass	Protein mass	Mascot	NA
prot_matches	Count of PSMs	Mascot	NA
prot_matches_sig	Count of PSMs that have significant scores under a proposed protein	Joint Mascot prot_matches_sig from individual data sources; PSMs with void reporter-ion intensity (of shared peptides) are included.	NA
prot_sequences	Count of distinct sequences	Mascot	NA
prot_sequences_sig	Count of distinct sequences that have significant scores under a proposed protein	Joint Mascot prot_sequences_sig from individual data sources; the counts may be greater than prot_sequences when peptides with different variable modifications are treated as different identities	NA
prot_len	The number of amino acid residues under a proposed protein	Mascot; or proteoQ if absent from Mascot PSM exports	NA
prot_cover	Protein sequence coverage	Calculated from the union of individual data sources	NA
prot_…	Additional protein keys from Mascot PSM exports	By users	NA
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	By each TMT experiment and LC/MS series; the counts exclude entries that are void in reporter-ion intensity or filtered by users	NA
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. prot_n_psm	NA
pep_seq_mod	pep_seq with variable modifications in the lower cases	See the help ?proteoQ::normPSM from an R console for nomenclatures; for example, “-._mAsGVAVSDGVIK.V”, with a methionine oxidation and a serine phosphorylation	NA
pep_query	Ordinal number of query after sorting by Mr	Mascot	NA
pep_rank	Peptide sequence match (PSM) rank. If two PSMs have same score they have the same rank.	Mascot	NA
pep_isbold	If grouping enabled, then a significant PSM. Otherwise, indicates this is the highest scoring protein that contains a match to this query.	Mascot	NA
pep_isunique	Peptide sequence is unique to hit (grouping off) or family member (grouping on)	Mascot	NA
pep_exp_mz	Observed or experimental m/z value	Mascot	NA
pep_exp_mr	Molecular mass calculated from experimental m/z value	Mascot	NA
pep_exp_z	Observed or experimental charge	Mascot	NA
pep_calc_mr	Molecular mass calculated from matched peptide sequence	Mascot	NA
pep_delta	pep_exp_mr – pep_calc_mr	Mascot	NA
pep_start	Ordinal position of first peptide residue in protein sequence	Cf. prot_len	NA
pep_end	Ordinal position of last peptide residue in protein sequence	Cf. prot_len	NA
pep_miss	Count of missed cleavage sites in peptide	Mascot	NA
pep_score	Mascot score for PSM	Mascot	NA
pep_expect	Expectation value for PSM	Mascot	NA
pep_res_before	Flanking residue on N-term side of peptide	Mascot	NA
pep_seq	One-letter representation of peptide sequences	See the help ?proteoQ::normPSM from an R console for nomenclatures. For example, “-._MASGVAVSDGVIK.V”, the acetylations of protein N-terminals is indicated by ’_’ and the flanking residues on the N- or C-terminal side of peptides separated by ‘.’	NA
pep_res_after	Flanking residue on C-term side of peptide	Mascot	NA
pep_var_mod	Variable modifications from all sources as list of names	Mascot	NA
pep_var_mod_pos	Variable modifications as a string of digits, e.g. ’0.0001000.0?. Non-zero digits identify mods according to key in export header. First and last positions are for terminus mods.	Mascot	NA
pep_summed_mod_pos	When two variable modifications occur at the same site, a string of digits defining the second mod	Mascot	NA
pep_local_mod_pos	Query-level variable modifications as a string of digits. The names of the mods will be listed in pep_var_mod	Mascot	NA
pep_scan_title	Scan title taken from peak list	Mascot	NA
pep_…	Additional peptide keys from Mascot PSM exports	By users	NA
pep_len	Number of amino acid residues in a peptide sequence		NA
pep_locprob	The highest probablity from Mascot site analysis for the variable modification sites	The second highest probablity, pep_locprob2, not shown directly but summarized under pep_locdiff; Cf. pep_var_mod_conf from Mascot	NA
pep_locdiff	pep_locprob – pep_locprob2		NA
pep_n_psm	Counts of significant PSMs in quantitation under a proposed peptide	Cf. prot_n_psm	NA
raw_file	MS file name(s) where peptides or proteins are identified		NA
gene	Protein gene name		NA
acc_type	The type of accession names	One of refseq_acc, uniprot_acc or uniprot_id	NA
uniprot_id	Uniprot ID	Optional for UniProt Fasta; the key will become uniprot_acc if the primary one is uniprot_id	NA
species	The species of a protein entry		NA
entrez	Protein Entrez ID		NA
kin_attr	The attribute of proteins being kinases	Optional at normPSM(annot_kinases = TRUE, ...)	NA
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. kin_attr	NA
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. kin_attr	NA
is_tryptic	Logical indicating if a sequence belongs to a canonical tryptic peptide	Optional when pep_start and pep_end are absent from Mascot PSMs	NA
I126 etc.	Reporter-ion intensity from MS/MS ion search	Mascot	NA
N_I126 etc.	Normalized reporter-ion intensity	The calibration factors for the alignment of log2R... are used to scale the reporter-ion intensity	NA
sd_log2_R126 etc.	Standard deviation of peptide log2FC	Calculated from contributing PSMs under each TMT channel	NA
R126 etc.	Linear FC relative to TMT-126		NA
log2_R126 etc.	log2FC in relative to the average intensity of reference(s) under each multiplex TMT	Relative to the row-mean intensity within each multiplex TMT if no reference(s) are present	NA
N_log2_R126 etc.	Median-centered log2_R...		NA

4.1.2 Peptides

Prior to significance tests, the primary peptide outputs with and without the imputation of NA values are summarized in Peptide.txt and Peptide_impNA.txt, respectively. The column keys therein are described in the following:

Header	Descrption	Note
prot_acc	Protein accession string	Mascot
prot_desc	Protein description taken from Fasta title line	Mascot
prot_mass	Protein mass	Mascot
prot_matches_sig	Count of PSMs that have significant scores under a proposed protein	Cf. PSM keys
prot_sequences_sig	Count of distinct sequences that have significant scores under a proposed protein	Cf. PSM keys
prot_len	The number of amino acid residues under a proposed protein	Cf. PSM keys
prot_cover	Protein sequence coverage	Cf. PSM keys
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	Joint results from individual PSM tables; the counts exclude entries that are void in reporter-ion intensity or filtered by users
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. prot_n_psm
pep_seq	One-letter representation of peptide sequences	Cf. PSM keys; the key will become pep_seq_mod at normPSM(group_psm_by = pep_seq_mod)
pep_seq_mod	pep_seq with variable modifications in the lower cases	Cf. PSM keys; the key will become pep_seq at normPSM(group_psm_by = pep_seq)
pep_n_psm	Counts of significant PSMs in quantitation under a proposed peptide	Cf. prot_n_psm
pep_isunique	Peptide sequence is unique to hit (grouping off) or family member (grouping on)	Mascot
pep_calc_mr	Molecular mass calculated from matched peptide sequence	Mascot
pep_start	Ordinal position of first peptide residue in protein sequence	Mascot; or proteoQ if absent from Mascot PSM exports
pep_end	Mascot: ordinal position of last peptide residue in protein sequence	Cf. pep_start
pep_miss	Count of missed cleavage sites in peptide	Mascot
pep_len	Number of amino acid residues in a peptide sequence	Cf. PSM keys
pep_rank	Peptide sequence match (PSM) rank. If two PSMs have same score they have the same rank.	Median description from PSMs
pep_isbold	If grouping enabled, then a significant PSM; otherwise, indicates this is the highest scoring protein that contains a match to this query.	Cf. pep_rank
pep_exp_mz	Observed or experimental m/z value	Cf. pep_rank
pep_exp_mr	Molecular mass calculated from experimental m/z value	Cf. pep_rank
pep_exp_z	Observed or experimental charge	Cf. pep_rank
pep_delta	pep_exp_mr – pep_calc_mr	Cf. pep_rank
pep_score	Mascot score for PSM	Cf. pep_rank
pep_locprob	The highest probablity from Mascot site analysis for the variable modification sites	Median description from PSMs
pep_locdiff	pep_locprob – pep_locprob2	Cf. PSM keys
pep_expect	Expectation value for PSM	Geometric-mean description from PSMs
pep_mod_protnt	Logical indicating if a sequence Protein N-terminal modification	Cf. help(normPSM) from an R console
pep_mod_protntac	Logical indicating if a sequence contains Protein N-terminal acetylation	v.s.
pep_mod_pepnt	Logical indicating if a sequence contains N-terminal modification	v.s.
pep_mod_m	Logical indicating if a sequence contains methionine oxidation	v.s.
pep_mod_n	Logical indicating if a sequence contains asparagine deamidation	v.s.
pep_mod_sty	Logical indicating if a sequence contains the phospholyration of serine, threonine or tyrosine	v.s.
pep_mod_pepct	Logical indicating if a sequence contains C-terminal modification	v.s.
pep_mod_protctam	Logical indicating if a sequence contains Protein C-terminal amidation	v.s.
pep_mod_protct	Logical indicating if a sequence contains Protein C-terminal modification	v.s.
pep_mean_raw	Mean log2_R (…) across samples	Reference and Empty samples excluded.
pep_mean_n	Mean N_log2FC(…) across samples	v.s.
pep_mean_z	Mean Z_log2FC(…) across samples	v.s.
gene	Protein gene name
acc_type	The type of accession names
uniprot_id	Uniprot ID	Cf. PSM keys
entrez	Protein Entrez ID
species	The species of a protein entry
kin_attr	The attribute of proteins being kinases	Cf. PSM keys
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. PSM keys
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. PSM keys
is_tryptic	Logical indicating if a sequence belongs to a canonical tryptic peptide	Cf. PSM keys
I… (…)	Reporter-ion intensity	Calculated from the descriptive statistics by method_psm_pep in PSM2Pep() for indicated samples
N_I… (…)	Normalized I… (…)	The calibration factors for the alignment of log2FC are used to scale the reporter-ion intensity
sd_log2_R (…)	Standard deviation of protein log2FC	Calculated from contributing peptides under each sample
log2_R (…)	log2FC relative to reference materials for indicated samples	Before normalization
N_log2_R (…)	Aligned log2_R (…) according to method_align in standPep() without scaling normalization
Z_log2_R (…)	N_log2_R (…) with scaling normalization

4.1.3 Proteins

Prior to significance tests, the primary protein outputs with and without the imputation of NA values are summarized in Protein.txt and Protein_impNA.txt, respectively. The corresponding column keys are described in the following:

Header	Descrption	Note
gene	Protein gene name
prot_cover	Protein sequence coverage	Cf. PSM keys
prot_acc	Protein accession string	Mascot
prot_desc	Protein description taken from Fasta title line	Mascot
prot_mass	Protein mass	Mascot
prot_matches_sig	Count of PSMs that have significant scores under a proposed protein	Cf. PSM keys
prot_sequences_sig	Count of distinct sequences that have significant scores under a proposed protein	Cf. PSM keys
prot_len	The number of amino acid residues under a proposed protein	Cf. PSM keys
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	Cf. Peptide keys
prot_n_uniqpsm	Count of unique, significant PSMs in quantitation under a proposed protein
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. Peptide keys
prot_n_uniqpep	Count of unique, significant peptide sequences in quantitation under a proposed protein
prot_mean_raw	Mean log2_R (…) across samples	Reference and Empty samples excluded.
prot_mean_n	Mean N_log2FC(…) across samples	v.s.
prot_mean_z	Mean Z_log2FC(…) across samples	v.s.
acc_type	The type of accession names
uniprot_id	Uniprot ID	Cf. PSM keys
entrez	Protein Entrez ID
species	The species of a protein entry
kin_attr	The attribute of proteins being kinases	Cf. PSM keys
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. PSM keys
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. PSM keys
I… (…)	Reporter-ion intensity	Calculated from the descriptive statistics by method_pep_prn in Pep2Prn() for indicated samples
N_I… (…)	Normalized I… (…)	Cf. Peptide keys
log2_R (…)	log2FC relative to reference materials for indicated samples	Cf. Peptide keys
N_log2_R (…)	Aligned log2_R (…) according to method_align in standPrn() without scaling normalization
Z_log2_R (…)	N_log2_R (…) with scaling normalization

4.2 MaxQuant

MaxQuant files shares the same folder structure as those of Mascot.

4.2.1 PSMs

The column keys are defined in MaxQuant with the following additions or modifications:

Header	Descrption	Note
prot_acc	Protein accession string	Proteins in MaxQuant
prot_desc	Protein description taken from Fasta title line
prot_mass	Protein mass
prot_len	The number of amino acid residues under a proposed protein
prot_cover	Protein sequence coverage	Calculated from the union of individual data sources
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	By each TMT experiment and LC/MS series; the counts exclude entries that are void in reporter-ion intensity or filtered by users
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. prot_n_psm
pep_seq	One-letter representation of peptide sequences	The acetylations of protein N-terminals is indicated by ’_’ and the flanking residues on the N- or C-terminal side of peptides separated by ‘.’, e.g. “-._MASGVAVSDGVIK.V”
pep_seq_mod	pep_seq with variable modifications in the lower cases	E.g. “-._mAsGVAVSDGVIK.V” with a methionine oxidation and a serine phosphorylation
pep_isunique	Peptide sequence is unique at the levels of protein groups, protein IDs or none	Cf. proteoQ help document via ?normPSM
pep_res_before	Flanking residue on N-term side of peptide
pep_start	Ordinal position of first peptide residue in protein sequence
pep_end	Ordinal position of last peptide residue in protein sequence
pep_res_after	Flanking residue on C-term side of peptide
pep_n_psm	Counts of significant PSMs in quantitation under a proposed peptide	Cf. prot_n_psm
raw_file	MS file name(s) where peptides or proteins are identified
m/z	The mass-over-charge of the precursor ion.	From MaxQuant
acc_type	The type of accession names	One of refseq_acc, uniprot_acc or uniprot_id
uniprot_id	Uniprot ID	Optional for UniProt Fasta; the key will become uniprot_acc if the primary one is uniprot_id
entrez	Protein Entrez ID
gene	Protein gene name
species	The species of a protein entry
kin_attr	The attribute of proteins being kinases	Optional at normPSM(annot_kinases = TRUE, ...)
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. kin_attr
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. kin_attr
is_tryptic	Logical indicating if a sequence belongs to a canonical tryptic peptide
…	More column keys from MaxQuant	Cf. http://www.coxdocs.org/doku.php?id=maxquant:table:msmstable
I126 etc.	Reporter-ion intensity	Corrected or uncorrected from MaxQuant; c.f. ?normPSM
N_I126 etc.	Normalized reporter-ion intensity	The calibration factors for the alignment of log2FC are used to scale the reporter-ion intensity
sd_log2_R126 etc.	Standard deviation of peptide log2FC	Calculated from contributing PSMs under each TMT channel
R126 etc.	Linear FC relative to TMT-126
log2_R126 etc.	log2FC in relative to the average intensity of reference(s) under each multiplex TMT	Relative to the row-mean intensity within each multiplex TMT if no reference(s) are present
N_log2_R126 etc.	Median-centered log2_R…

4.2.2 Peptides

The column keys in peptide tables are described below:

Header	Descrption	Note
prot_acc	Protein accession string	Cf. PSM keys
prot_desc	Protein description taken from Fasta title line
prot_mass	Protein mass
prot_len	The number of amino acid residues under a proposed protein
prot_cover	Protein sequence coverage	Cf. PSM keys
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	Joint results from individual PSM tables; the counts exclude entries that are void in reporter-ion intensity or filtered by users
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. prot_n_psm
pep_seq	One-letter representation of peptide sequences	Cf. PSM keys; the key will become pep_seq_mod at normPSM(group_psm_by = pep_seq_mod)
pep_seq_mod	pep_seq with variable modifications in the lower cases	Cf. PSM keys; the key will become pep_seq at normPSM(group_psm_by = pep_seq)
pep_n_psm	Counts of significant PSMs in quantitation under a proposed peptide	Cf. prot_n_psm
pep_isunique	Peptide sequence is unique at the levels of protein groups, protein IDs or none	Cf. PSM keys
pep_start	Ordinal position of first peptide residue in protein sequence
pep_end	Mascot: ordinal position of last peptide residue in protein sequence
pep_mod_protnt	Logical indicating if a sequence contains Protein N-terminal modification	Cf. ?normPSM from an R console
pep_mod_protntac	Logical indicating if a sequence contains Protein N-terminal acetylation	v.s.
pep_mod_pepnt	Logical indicating if a sequence contains N-terminal modification	v.s.
pep_mod_m	Logical indicating if a sequence contains methionine oxidation	v.s.
pep_mod_n	Logical indicating if a sequence contains asparagine deamidation	v.s.
pep_mod_sty	Logical indicating if a sequence contains the phospholyration of serine, threonine or tyrosine	v.s.
pep_mod_pepct	Logical indicating if a sequence contains C-terminal modification	v.s.
pep_mod_protctam	Logical indicating if a sequence contains Protein C-terminal amidation	v.s.
pep_mod_protct	Logical indicating if a sequence contains Protein C-terminal modification	v.s.
pep_mean_raw	Mean log2_R (…) across selected samples	Sample selection vai standPep(col_select = ...)
pep_mean_n	Mean N_log2FC(…) across selected samples	v.s.
pep_mean_z	Mean Z_log2FC(…) across selected samples	v.s.
gene	Protein gene name
m/z	The mass-over-charge of the precursor ion.	Cf. PSM keys
acc_type	The type of accession names	Cf. PSM keys
entrez	Protein Entrez ID
uniprot_id	Uniprot ID	Cf. PSM keys
species	The species of a protein entry
kin_attr	The attribute of proteins being kinases	Cf. PSM keys
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. PSM keys
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. PSM keys
is_tryptic	Logical indicating if a sequence belongs to a canonical tryptic peptide
kin_attr	The attribute of proteins being kinases	Cf. PSM keys
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. PSM keys
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. PSM keys
…	More column keys from MaxQuant	Median description for the keys of “Score”, “PEP”, “Charge”, “Mass”, “PIF”, “Fraction of total spectrum”, “Mass error [ppm]”, “Mass error [Da]”, “Base peak fraction”, “Precursor Intensity”, “Precursor Apex Fraction”, “Intensity coverage”, “Peak coverage”, “Combinatorics”
I… (…)	Reporter-ion intensity	Calculated from the descriptive statistics by method_psm_pep in PSM2Pep() for indicated samples
N_I… (…)	Normalized I… (…)	The calibration factors for the alignment of log2FC are used to scale the reporter-ion intensity
sd_log2_R (…)	Standard deviation of protein log2FC	Calculated from contributing peptides under each sample
log2_R (…)	log2FC relative to reference materials for indicated samples	Before normalization
N_log2_R (…)	Aligned log2_R (…) according to method_align in standPep() without scaling normalization
Z_log2_R (…)	N_log2_R (…) with scaling normalization

4.2.3 Proteins

The corresponidng column keys are described below:

Header	Descrption	Note
gene	Protein gene name
prot_cover	Protein sequence coverage	Cf. PSM keys
prot_acc	Protein accession string	Cf. PSM keys
prot_desc	Protein description taken from Fasta title line
prot_mass	Protein mass
prot_len	The number of amino acid residues under a proposed protein
prot_n_psm	Count of significant PSMs in quantitation under a proposed protein	Cf. Peptide keys
prot_n_uniqpsm	Count of unique, significant PSMs in quantitation under a proposed protein
prot_n_pep	Count of significant peptide sequences in quantitation under a proposed protein	Cf. Peptide keys
prot_n_uniqpep	Count of unique, significant peptide sequences in quantitation under a proposed protein
prot_mean_raw	Mean log2_R (…) across samples	Reference and Empty samples excluded.
prot_mean_n	Mean N_log2FC(…) across samples	v.s.
prot_mean_z	Mean Z_log2FC(…) across samples	v.s.
m/z	The mass-over-charge of the precursor ion.	Cf. PSM keys
acc_type	The type of accession names
uniprot_id	Uniprot ID	Cf. PSM keys
entrez	Protein Entrez ID
species	The species of a protein entry
kin_attr	The attribute of proteins being kinases	Cf. PSM keys
kin_class	The classes of kinases, e.g., TK, TKL…	Cf. PSM keys
kin_order	The order of “kin_class” from the kinase tree diagram	Cf. PSM keys
is_tryptic	Logical indicating if a sequence belongs to a canonical tryptic peptide
…	More column keys from MaxQuant	Median description from peptide data
I… (…)	Reporter-ion intensity	Calculated from the descriptive statistics by method_pep_prn in Pep2Prn() for indicated samples
N_I… (…)	Normalized I… (…)	Cf. Peptide keys
log2_R (…)	log2FC relative to reference materials for indicated samples	Before normalization
N_log2_R (…)	Aligned log2_R (…) according to method_align in standPrn() without scaling normalization
Z_log2_R (…)	N_log2_R (…) with scaling normalization

5 Appendix

5.1 Mascot PSM tables

See notes here.

5.2 Vararg table

See notes here.

References

Philipp, Martins. 2018. “Reproducible Workflow for Multiplexed Deep-Scale Proteome and Phosphoproteome Analysis of Tumor Tissues by Liquid Chromatography-Mass Spectrometry.” Nature Protocols 13 (7): 1632–61. https://doi.org/10.1038/s41596-018-0006-9.

Wickham, Hadley. 2019. Advanced R. 2nd ed. Chapman & Hall/CRC. https://adv-r.hadley.nz/.

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1. To cite this work: (2019) R package proteoQ for Quantitative Proteomics Using Tandem Mass Tags. https://github.com/qzhang503/proteoQ.↩︎

2. For a specific version, for example 1.2.2.2: devtools::install_github("qzhang503/proteoQ@1.2.2.2")↩︎

3. If not, try devtools::install_github("qzhang503/proteoQDA")↩︎

4. See https://www.uniprot.org/proteomes/ for lists of UniProt proteomes↩︎

5. See Appendix for notes on the export of Mascot PSMs.↩︎

6. To extract the names of RAW MS files under a raw_dir folder: extract_raws(raw_dir). Very occasionally, there may be RAW files without PSM contributions. In this case, the file names will be shown as missing by the program and need to be removed from expt_smry.xlsx or frac_smry.xlsx. The function extract_psm_raws(dat_dir) was developed to extract the list of RAW files that are actually present in PSM files.↩︎

7. On top of technical variabilities, the ranges of CV may be further subject to the choice of reference materials. Examples are available in Lab 3.1.↩︎

8. Density kernel estimates can occasionally capture spikes in the profiles of log2FC during data alignment. Users will need to inspect the alignment of ratio histograms and may optimize the data normalization in full with different combinations of tuning parameters or in part against a subset of samples, before proceeding to the next steps.↩︎

9. standPep() will report log2FC results both before and after the scaling of standard deviations.↩︎

10. The default is scale_log2r = TRUE throughout the package. When calling functions involved parameter scale_log2r, users can specify explicitly scale_log2r = FALSE if needed, or more preferably define its value under the global environment.↩︎

11. A lab section is under construction.↩︎

12. A lab is under construction.↩︎

13. This will work as GO terms of human start with hs_ and KEGG terms with hsa.↩︎

14. Details on the notation of peptide modifications can be found via ?normPSM.↩︎